# Fuzzy set theory

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Fuzzy sets are sets whose elements have degrees of membership. Fuzzy sets have been introduced by Lotfi A. Zadeh (1965) as an extension of the classical notion of set.[1] In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition — an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the indicator functions of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take values 0 or 1.[2]

## Definition Edit

A fuzzy set is a pair $(A, m)$ where $A$ is a set and $m : A \rightarrow [0,1]$.

For each $x\in A$, $m(x)$ is the grade of membership of $x$. $x\in (A, m)\iff x\in A\wedge m(x)\neq 0$. If $A=\{x_1,...,x_n\}$ the fuzzy set $(A, m)$ can be denoted $\{m(x_1)/x_1,...,m(x_n)/x_n\}$.

An element mapping to the value 0 means that the member is not included in the fuzzy set, 1 describes a fully included member. Values strictly between 0 and 1 characterize the fuzzy members.[3]

Sometimes, a more general definition is used, where membership functions take values in an arbitrary fixed algebra or structure $L$; usually it is required that $L$ be at least a poset or lattice. The usual membership functions with values in [0, 1] are then called [0, 1]-valued membership functions. This generalization was first considered in 1967 by Joseph Goguen, who was a student of Zadeh.[4]

### Fuzzy logic Edit

Main article: Fuzzy logic

As an extension of the case of multi-valued logic, valuations ($\mu : \mathit{V}_o \to \mathit{W}$) of propositional variables ($\mathit{V}_o$) into a set of membership degrees ($\mathit{W}$) can be thought of as membership functions mapping predicates into fuzzy sets (or more formally, into an ordered set of fuzzy pairs, called a fuzzy relation). With these valuations, many-valued logic can be extended to allow for fuzzy premises from which graded conclusions may be drawn.[5]

This extension is sometimes called "fuzzy logic in the narrow sense" as opposed to "fuzzy logic in the wider sense," which originated in the engineering fields of automated control and knowledge engineering, and which encompasses many topics involving fuzzy sets and "approximated reasoning."[6]

Industrial applications of fuzzy sets in the context of "fuzzy logic in the wider sense" can be found at fuzzy logic.

### Fuzzy number Edit

A fuzzy number is a convex, normalized fuzzy set $\tilde{\mathit{A}}\subseteq\mathbb{R}$ whose membership function is at least segmentally continuous and has the functional value $\mu_{A}(x)=1$ at precisely one element. This can be likened to the funfair game "guess your weight," where someone guesses the contestant's weight, with closer guesses being more correct, and where the guesser "wins" if he or she guesses near enough to the contestant's weight, with the actual weight being completely correct (mapping to 1 by the membership function).

### Fuzzy interval Edit

A fuzzy interval is an uncertain set $\tilde{\mathit{A}}\subseteq\mathbb{R}$ with a mean interval whose elements possess the membership function value $\mu_{A}(x)=1$. As in fuzzy numbers, the membership function must be convex, normalized, at least segmentally continuous.[7]

## ReferencesEdit

1. # L.A. Zadeh (1965) Fuzzy sets. Information and Control 8 (3) 338--353<
2. D. Dubois and H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York.
3. Goguen, Joseph A., 1967, "L-fuzzy sets". Journal of Mathematical Analysis and Applications 18: 145–174
4. Gottwald, Siegfried, 2001. A Treatise on Many-Valued Logics. Baldock, Hertfordshire, England: Research Studies Press Ltd., ISBN 978-0863802621
5. "The concept of a linguistic variable and its application to approximate reasoning," Information Sciences 8: 199–249, 301–357; 9: 43–80.
6. "Fuzzy sets as a basis for a theory of possibility," Fuzzy Sets and Systems 1: 3–28

### BooksEdit

• Kay, P., & McDaniel, C. K. (1997). The linguistic significance of the meanings of basic color terms. Cambridge, MA: The MIT Press.
• Magdalena, L. (1997). A first approach to a taxonomy of fuzzy-neural systems. Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
• Reyna, V. F., & Brainerd, C. J. (1993). Fuzzy memory and mathematics in the classroom. Amsterdam, Netherlands: North-Holland/Elsevier Science Publishers.

### PapersEdit

• Ackerman, B. (1998). Children's false memories: A test of the dissociability of cognitive and social processes: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 178-183.
• Acosta, L., Marichal, G. N., Moreno, L., Mendez, J. A., & Rodrigo, J. J. (2000). Obstacle avoidance using the human operator experience for a mobile robot: Journal of Intelligent & Robotic Systems Vol 27(4) Apr 2000, 305-319.
• Alami, J., & El Imrani, A. (2008). Dielectric composite multimodal optimization using a multipopulation cultural algorithm: Intelligent Data Analysis Vol 12(4) 2008, 359-378.
• Alliger, G. M., Feinzig, S. L., & Janak, E. A. (1993). Fuzzy sets and personnel selection: Discussion and an application: Journal of Occupational and Organizational Psychology Vol 66(2) Jun 1993, 163-169.
• Alonso, J. M., Magdalena, L., Guillaume, S., Sotelo, M. A., Bergasa, L. M., Ocana, M., et al. (2007). Knowledge-based intelligent diagnosis of ground robot collision with non detectable obstacles: Journal of Intelligent & Robotic Systems Vol 48(4) Apr 2007, 539-566.
• Apolloni, B., Malchiodi, D., Orovas, C., Palmas, G., & Sperduti, A. (2002). From synapses to rules: Cognitive Systems Research Vol 3(1-4) Dec 2002, 167-201.
• Apolloni, B., Zamponi, G., & Zanaboni, A. M. (1998). Learning fuzzy decision trees: Neural Networks Vol 11(5) Jul 1998, 885-895.
• Archer, N. P., & Wang, S. (1991). Fuzzy set representation of neural network classification boundaries: IEEE Transactions on Systems, Man, & Cybernetics Vol 21(4) Jul-Aug 1991, 735-742.
• Arfi, B. (2006). Linguistic Fuzzy-Logic Game Theory: Journal of Conflict Resolution Vol 50(1) Feb 2006, 28-57.
• Arfi, B. (2006). Linguistic Fuzzy-Logic Social Game of Cooperation: Rationality and Society Vol 18(4) Nov 2006, 471-537.
• Bai, M. R., Hsiao, I., Tsai, H., & Lin, C. (2000). Development of an on-line diagnosis system for rotor vibration via model-based intelligent inference: Journal of the Acoustical Society of America Vol 107(1) Jan 2000, 315-323.
• Baptista, L. F., Sousa, J. M., & Sa Da Costa, J. (2001). Force control of robotic manipulators using a fuzzy predictive approach: Journal of Intelligent & Robotic Systems Vol 30(4) Apr 2001, 359-376.
• Barai, R. K., & Nonami, K. (2008). Locomotion control of a hydraulically actuated hexapod robot by robust adaptive fuzzy control with self-tuned adaptation gain and dead zone fuzzy pre-compensation: Journal of Intelligent & Robotic Systems Vol 53(1) Sep 2008, 35-56.
• Bell, P. M., & Crumpton, L. (1997). A fuzzy linguistic model for the prediction of carpal tunnel syndrome risks in an occupational environment: Ergonomics Vol 40(8) Aug 1997, 790-799.
• Beringer, D. B. (2002). Applying performance-controlled systems, fuzzy logic, and fly-by-wire controls to general aviation: FAA Office of Aviation Medicine Reports DOT/FAA/AM-02/7 May 2002, 11 p.
• Bezdek, J. C. (1995). Hybrid modeling in pattern recognition and control: Knowledge-Based Systems Vol 8(6) Dec 1995, 359-371.
• Bhandarkar, S. M. (1994). A fuzzy probabilistic model for the Generalized Hough Transform: IEEE Transactions on Systems, Man, & Cybernetics Vol 24(5) May 1994, 745-759.
• Bien, Z. Z., & Lee, H.-E. (2007). Effective learning system techniques for human-robot interaction in service environment: Knowledge-Based Systems Vol 20(5) Jun 2007, 439-456.
• Billot, A. (1991). Aggregation of preferences: The fuzzy case: Theory and Decision Vol 30(1) Jan 1991, 51-93.
• Blazic, S., & Skrjanc, I. (2007). Design and stability analysis of fuzzy model-based predictive control--A case study: Journal of Intelligent & Robotic Systems Vol 49(3) Jul 2007, 279-292.
• Bordogna, G., Fedrizzi, M., & Pasi, G. (1997). A linguistic modeling of consensus in group decision making based on OWA operators: IEEE Transactions on Systems, Man, & Cybernetics Vol 27(1) Jan 1997, 126-132.
• Botteldooren, D., & Verkeyn, A. (2002). Fuzzy models for accumulation of reported community noise annoyance from combined sources: Journal of the Acoustical Society of America Vol 112(4) Oct 2002, 1496-1508.
• Bouwmeester, S., & Sijtsma, K. (2007). Latent class modeling of phases in the development of transitive reasoning: Multivariate Behavioral Research Vol 42(3) 2007, 457-480.
• Bouwmeester, S., Vermunt, J. K., & Sijtsma, K. (2007). Development and individual differences in transitive reasoning: A fuzzy trace theory approach: Developmental Review Vol 27(1) Mar 2007, 41-74.
• Boyue, L., & Xiting, H. (2003). A Research on the Structure of Temporal Hierarchical Network of Fuzzy Groups: Psychological Science (China) Vol 26(6) Nov 2003, 979-982.
• Brackstone, M. (2000). Examination of the use of fuzzy sets to describe relative speed perception: Ergonomics Vol 43(4) Apr 2000, 528-542.
• Brainerd, C. J., & Reyna, V. F. (1990). Inclusion illusions: Fuzzy-trace theory and perceptual salience effects in cognitive development: Developmental Review Vol 10(4) Dec 1990, 365-403.
• Brainerd, C. J., & Reyna, V. F. (1998). Fuzzy-trace theory and children's false memories: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 81-129.
• Branzeia, R., Dimitrov, D., & Tijs, S. (2004). Egalitarianism in convex fuzzy games: Mathematical Social Sciences Vol 47(3) May 2004, 313-325.
• Budescu, D. V., Zwick, R., Wallsten, T. S., & Erev, I. (1990). Integration of linguistic probabilities: International Journal of Man-Machine Studies Vol 33(6) Dec 1990, 657-676.
• Buhusi, C. V. (2000). The across-fiber pattern theory and fuzzy logic: A matter of taste: Physiology & Behavior Vol 69(1/2) Apr 2000, 97-106.
• Burns, D. J., Jenkins, C. L., & Dean, E. E. (2007). Falsely recalled items are rich in item-specific information: Memory & Cognition Vol 35(7) Oct 2007, 1630-1640.
• Campbell, C. S., & Massaro, D. W. (1997). Perception of visible speech: Influence of spatial quantization: Perception Vol 26(5) 1997, 627-644.
• Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991). Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system: Neural Networks Vol 4(6) 1991, 759-771.
• Castellano, G., Fanelli, A. M., Mencar, C., & Frasconi, P. (2002). A neuro-fuzzy network to generate human-understandable knowledge from data: Cognitive Systems Research Vol 3(1-4) Dec 2002, 125-144.
• Castro, J. L. (1995). Fuzzy logic controllers are universal approximators: IEEE Transactions on Systems, Man, & Cybernetics Vol 25(4) Apr 1995, 629-635.
• Ceci, S. J., & Bruck, M. (1998). The ontogeny and durability of true and false memories: A fuzzy trace account: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 165-169.
• Center, B., & Verma, B. P. (1998). Fuzzy logic for biological and agricultural systems: Artificial Intelligence Review Vol 12(1-3) Feb 1998, 213-225.
• Chakraborty, C., & Chakraborty, D. (2007). Fuzzy rule base for consumer trustworthiness in Internet marketing: An interactive fuzzy rule classification approach: Intelligent Data Analysis Vol 11(4) 2007, 339-353.
• Chen, S.-J., & Chen, S.-M. (2007). Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers: Applied Intelligence Vol 26(1) Feb 2007, 1-11.
• Chen, S.-M., Lee, S.-H., & Lee, C.-H. (2001). A new method for generating fuzzy rules from numerical data for handling classification problems: Applied Artificial Intelligence Vol 15(7) Aug 2001, 645-664.
• Chen, Y.-T., & Jeng, B. (2005). MFILM: A multi-dimensional fuzzy inductive learning method: Journal of Experimental & Theoretical Artificial Intelligence Vol 17(3) Sep 2005, 267-281.
• Cheng, C.-B., Chan, C.-C. H., & Lin, K.-C. (2006). Intelligent agents for e-marketplace: Negotiation with issue trade-offs by fuzzy inference systems: Decision Support Systems Vol 42(2) Nov 2006, 626-638.
• Chi, Z., & Yan, H. (1995). Handwritten numeral recognition using a small number of fuzzy rules with optimized defuzzification parameters: Neural Networks Vol 8(5) 1995, 821-827.
• Choi, D.-Y. (1999). A new aggregation method in a fuzzy environment: Decision Support Systems Vol 25(1) Feb 1999, 39-51.
• Choi, D.-Y. (2003). Enhancing the power of Web search engines by means of fuzzy query: Decision Support Systems Vol 35(1) Apr 2003, 31-44.
• Chortaras, A., Stamou, G., & Stafylopatis, A. (2008). Connectionist weighted fuzzy logic programs: Neurocomputing: An International Journal Vol 71(13-15) Aug 2008, 2456-2469.
• Colbert, J. M., & McBride, D. M. (2007). Comparing decay rates for accurate and false memories in the DRM paradigm: Memory & Cognition Vol 35(7) Oct 2007, 1600-1609.
• Cooper, J. A., Ferson, S., & Ginzburg, L. (1996). Hybrid processing of stochastic and subjective uncertainty data: Risk Analysis Vol 16(6) Dec 1996, 785-791.
• Cornelis, C., De Cock, M., & Kerre, E. E. (2003). Intuitionistic fuzzy rough sets: At the crossroads of imperfect knowledge: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 20(5) Nov 2003, 260-270.
• Cowan, N. (1998). Children's memories according to fuzzy-trace theory: An endorsement of the theory's purpose and some suggestions to improve its application: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 144-154.
• Criado, F., Gachechiladze, T., Jorjiashvili, N., Mandjaparashvili, T., Meladze, H., Sirbiladze, G., et al. (2004). Fuzzy Analysis (Image Construction) of the Language Structure on a Finite Set of Insufficient Data: Journal of Quantitative Linguistics Vol 11(1-2) Apr-Aug 2004, 93-132.
• Crockett, K. A., Bandar, Z., Fowdar, J., & O'Shea, J. (2006). Genetic tuning of fuzzy inference within fuzzy classifier systems: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 23(2) May 2006, 63-82.
• Crowther, C. S., Batchelder, W. H., & Hu, X. (1995). A measurement-theoretic analysis of the fuzzy logic model of perception: Psychological Review Vol 102(2) Apr 1995, 396-408.
• de Carvalho, L. M. F., Nassar, S. M., de Azevedo, F. M., de Carvalho, H. J. T., & Dani, C. A. (2004). Analysis of learning models to diagnose paroxysmal events: Revista Brasileira de Neurologia Vol 40(2) Apr-Jun 2004, 39-44.
• de Gelder, B., & Vroomen, J. (2000). Bimodal emotion perception: Integration across separate modalities, cross-modal perceptual grouping or perception of multimodal events? : Cognition & Emotion Vol 14(3) May 2000, 321-324.
• de Juan, J. M. (1987). Arrow's theorem under fuzzy rationality: Behavioral Science Vol 32(4) Oct 1987, 267-273.
• Derry, S. J., & Hawkes, L. W. (1993). Local cognitive modeling of problem-solving behavior: An application of fuzzy theory. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
• Di Lascio, L., Fischetti, E., & Gisolfi, A. (1999). A fuzzy-based approach to stereotype selection in hypermedia: User Modeling and User-Adapted Interaction Vol 9(4) 1999, 285-320.
• Dieulot, J. Y., & Borne, P. (2002). Parameter tuning of stable fuzzy controllers: Journal of Intelligent & Robotic Systems Vol 33(3) Mar 2002, 301-312.
• Dorsey, D. W., & Coovert, M. D. (2003). Mathematical modeling of decision making: A soft and fuzzy approach to capturing hard decisions: Human Factors Vol 45(1) Spr 2003, 117-135.
• Dozier, G. (2000). Commentary on:"A nonlinear, GA-optimized, fuzzy logic system for the evaluation of multisource biofunctional intelligence." Journal of Mind and Behavior Vol 21(1-2) Win-Spr 2000, 149-151.
• Drobics, M., Bodenhofer, U., & Winiwarter, W. (2002). Mining clusters and corresponding interpretable descriptions -- A three-stage approach: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 19(4) Sep 2002, 224-234.
• Du, Y., Lewis, C., & Pashley, P. J. (1993). Computerized mastery testing using fuzzy set decision theory: Applied Measurement in Education Vol 6(3) 1993, 181-193.
• Dubitzky, W., Schuster, A., Hughes, J., & Bell, D. (1997). An advanced case-knowledge architecture based on fuzzy objects: Applied Intelligence Vol 7(3) Jul 1997, 187-204.
• Dunn, J. C. (2000). Model complexity: The fit to random data reconsidered: Psychological Research Vol 63(2) Jun 2000, 174-182.
• d'Ydewalle, G., & Auwers, T. (1994). Orthographic redundancy in letter recognition: Orthographic neighbourhood or orthographic context? : European Journal of Cognitive Psychology Vol 6(3) Sep 1994, 287-310.
• Er, M. J., & Zhou, Y. (2008). A novel framework for automatic generation of fuzzy neural networks: Neurocomputing: An International Journal Vol 71(4-6) Jan 2008, 584-591.
• Erickson, R. P., Di Lorenzo, P. M., & Woodbury, M. A. (1994). Classification of taste responses in brain stem: Membership in fuzzy sets: Journal of Neurophysiology Vol 71(6) Jun 1994, 2139-2150.
• Esmaeili, V., Assareh, A., Shamsollahi, M. B., Moradi, M. H., & Arefian, N. M. (2008). Estimating the depth of anesthesia using fuzzy soft computation applied to EEG features: Intelligent Data Analysis Vol 12(4) 2008, 393-407.
• Friedman, D., Massaro, D. W., Kitzis, S. N., & Cohen, M. M. (1995). A comparison of learning models: Journal of Mathematical Psychology Vol 39(2) Jun 1995, 164-178.
• Geethanjali, M., & Slochanal, S. M. R. (2008). A combined adaptive network and fuzzy inference system (ANFIS) approach for overcurrent relay system: Neurocomputing: An International Journal Vol 71(4-6) Jan 2008, 895-903.
• Gehrman, P., Matt, G. E., Turingan, M., Dinh, Q., & Ancoli-Israel, S. (2002). Towards an understanding of self-reports of sleep: Journal of Sleep Research Vol 11(3) 2002, 229-236.
• Gil, M. A., & Jain, P. (1992). Comparison of experiments in statistical decision problems with fuzzy utilities: IEEE Transactions on Systems, Man, & Cybernetics Vol 22(4) Jul-Aug 1992, 662-670.
• Goertz, G., & Mahoney, J. (2005). Two-Level Theories and Fuzzy-Set Analysis: Sociological Methods & Research Vol 33(4) May 2005, 497-538.
• Golob, M., & Tovornik, B. (2008). Input-output modelling with decomposed neuro-fuzzy ARX model: Neurocomputing: An International Journal Vol 71(4-6) Jan 2008, 875-884.
• Gordon, A. D., & Vichi, M. (2001). Fuzzy partition models for fitting a set of partitions: Psychometrika Vol 66(2) Jun 2001, 229-247.
• Gravani, M. N., Hadjileontiadou, S. J., Nikolaidou, G. N., & Hadjileontiadis, L. J. (2007). Professional learning: A fuzzy logic-based modelling approach: Learning and Instruction Vol 17(2) Apr 2007, 235-252.
• Grobelny, J., Karwowski, W., & Zurada, J. (1995). Applications of fuzzy-based linguistic patterns for the assessment of computer screen design quality: International Journal of Human-Computer Interaction Vol 7(3) Jul-Sep 1995, 193-212.
• Guesgen, H. W., Hertzberg, J., Lobb, R., & Mantler, A. (2003). Buffering fuzzy maps in GIS: Spatial Cognition and Computation Vol 3(2-3) 2003, 205-220.
• Hage, F. M. (2007). Constructivism, fuzzy sets and (very) small-N: Revisiting the conditions for communicative action: Journal of Business Research Vol 60(5) May 2007, 512-521.
• Hancock, P. A., Masalonis, A. J., & Parasuraman, R. (2000). On the theory of fuzzy signal detection: Theoretical and practical considerations: Theoretical Issues in Ergonomics Science Vol 1(3) Jul 2000, 207-230.
• Haslam, N., & Cleland, C. (2002). Taxometric analysis of fuzzy categories: A Monte Carlo study: Psychological Reports Vol 90(2) Apr 2002, 401-404.
• Hayashi, F., & Oda, T. (1996). The five-factor model of personality traits: An examination based on fuzzy-set theory: Japanese Journal of Psychology Vol 66(6) Feb 1996, 401-408.
• Helgason, C. M., & Jobe, T. H. (1998). The fuzzy cube and causal efficacy: Representation of concomitant mechanisms in stroke: Neural Networks Vol 11(3) Apr 1998, 549-555.
• Herrera, F., Herrera-Viedma, E., & Verdegay, J. L. (1996). A linguistic decision process in group decision making: Group Decision and Negotiation Vol 5(2) Mar 1996, 165-176.
• Herrera, L. J., Pomares, H., Rojas, I., Guillen, A., Gonzalez, J., Awad, M., et al. (2007). Multigrid-based fuzzy systems for time series prediction: CATS competition: Neurocomputing: An International Journal Vol 70(13-15) Aug 2007, 2410-2425.
• Herrero, F. J., Cuesta, M., & Grossi, F. J. (1991). Factor analysis, fuzzy logic, and expert systems: Comparison of factor structures from different samples: Analisis y Modificacion de Conducta Vol 17(52) 1991, 239-252.
• Hesketh, B., Hesketh, T., Hansen, J.-I., & Goranson, D. (1995). Use of fuzzy variables in developing new scales from the Strong Interest Inventory: Journal of Counseling Psychology Vol 42(1) Jan 1995, 85-99.
• Hiao, S.-W. (1994). Fuzzy set theory on car-color design: Color Research and Application Vol 19(3) Jun 1994, 202-213.
• Hodges, J., Bridges, S., Sparrow, C., Weerakkody, G., Tang, B., Jun, C., et al. (2001). Assessing the performance of a waste characterization expert system: Applied Artificial Intelligence Vol 15(4) Apr 2001, 385-396.
• Homaifar, A., Copalan, V., Dismuke, L., & Iran-Nejad, A. (2000). A nonlinear, GA-optimized, fuzzy logic system for the evaluation of multisource biofunctional intelligence: Journal of Mind and Behavior Vol 21(1-2) Win-Spr 2000, 137-147.
• Horng, Y.-J., Chen, S.-M., & Lee, C.-H. (2003). Automatically constructing multi-relationship fuzzy concept networks for document retrieval: Applied Artificial Intelligence Vol 17(4) Apr 2003, 303-328.
• Hougaard, J. L., & Keiding, H. (1996). Representation of preferences on fuzzy measures by a fuzzy integral: Mathematical Social Sciences Vol 31(1) Feb 1996, 1-17.
• Howe, M. L. (1998). When distinctiveness fails, false memories prevail: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 170-177.
• Hsiao, S.-W. (1995). A systematic method for color planning in product design: Color Research and Application Vol 20(3) Jun 1995, 191-205.
• Hu, Y.-C. (2005). Finding simplified fuzzy if-then rules for function approximation problems using a fuzzy data mining approach: Applied Artificial Intelligence Vol 19(6) Jul 2005, 601-619.
• Huang, M.-J. (1999). A fuzzy student modeling with two intelligent agents: Journal of Educational Computing Research Vol 21(1) 1999, 99-113.
• Huang, X., & Sun, C. (1991). An analysis of characteristics for designating value to temporal qualifiers semantics: Acta Psychologica Sinica Vol 23(3) Fal 1991, 243-249.
• Huang, X., & Sun, C. (1992). Application of relation of fuzzy partial ordering in psychological research: Acta Psychologica Sinica Vol 24(2) Sum 1992, 135-141.
• Huang, X., Sun, C., & Hu, W. (1998). Psychological structure of time: Psychological Science (China) Vol 21(1) 1998, 1-4, 16.
• Huang, Y. J., Chang, S. H., & Kuo, T. C. (2008). Robust fuzzy output sliding control without the requirement of state measurement: Journal of Intelligent & Robotic Systems Vol 53(2) Oct 2008, 169-182.
• Hung, L.-C., & Chung, H.-Y. (2006). Fuzzy sliding-mode control with rule adaptation for nonlinear systems: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 23(4) Sep 2006, 226-240.
• Huttenlocher, J., & Hedges, L. V. (1994). Combining graded categories: Membership and typicality: Psychological Review Vol 101(1) Jan 1994, 157-165.
• Inglis, I. R., Forkman, B., & Lazarus, J. (1997). Free food or earned food? A review and fuzzy model of contrafreeloading: Animal Behaviour Vol 53(6) Jun 1997, 1171-1191.
• Ioannidou, I. A., Paraskevopoulos, S., & Tzionas, P. (2006). An interactive computer graphics interface for the introduction of fuzzy inference in environmental education: Interacting with Computers Vol 18(4) Jul 2006, 683-708.
• Israelski, E. W. (1989). Somewhat of a Treatise on Fuzzy Set Theory Applications: PsycCRITIQUES Vol 34 (7), Jul, 1989.
• Izquierdo, J. M. C., Dimitriadis, Y. A., Sanchez, E. G., & Coronado, J. L. (2001). Learning from noisy information in FasArt and FasBack neuro-fuzzy systems: Neural Networks Vol 14(4-5) May 2001, 407-425.
• Jagielska, I., Matthews, C., & Whitfort, T. (1999). An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems: Neurocomputing: An International Journal Vol 24(1-3) Feb 1999, 37-54.
• Jang, J.-s. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system: IEEE Transactions on Systems, Man, & Cybernetics Vol 23(3) May-Jun 1993, 665-685.
• Jeng, B., Jeng, Y.-M., & Liang, T.-P. (1997). FILM: A Fuzzy Inductive Learning Method for automated knowledge acquisition: Decision Support Systems Vol 21(2) Oct 1997, 61-73.
• Jones, R. W., Lowe, A., & Harrison, M. J. (2002). A framework for intelligent medical diagnosis using the theory of evidence: Knowledge-Based Systems Vol 15(1-2) Jan 2002, 77-84.
• Joyce, D., & Kennison, J. (2006). Mathematical concepts for analyzing ambiguous situations: Estudios de Psicologia Vol 27(1) 2006, 85-100.
• Juang, C.-F., & Chung, I. F. (2007). Recurrent fuzzy network design using hybrid evolutionary learning algorithms: Neurocomputing: An International Journal Vol 70(16-18) Oct 2007, 3001-3010.
• Kaburlasos, V. G., & Petridis, V. (2000). Fuzzy lattice neurocomputing (FLN) models: Neural Networks Vol 13(10) Dec 2000, 1145-1169.
• Kamp, H., & Partee, B. (1995). Prototype theory and compositionality: Cognition Vol 57(2) Nov 1995, 129-191.
• Kang, H., & Vachtsevanos, G. (1993). Fuzzy hypercubes: Linguistic learning/reasoning systems for intelligent control and identification: Journal of Intelligent & Robotic Systems Vol 7(2) Apr 1993, 215-232.
• Karwowski, W. (1991). Complexity, fuzziness, and ergonomic incompatibility issues in the control of dynamic work environments: Ergonomics Vol 34(6) Jun 1991, 671-686.
• Karwowski, W., Kosiba, E., Benabdallah, S., & Salvendy, G. (1990). A framework for development of fuzzy GOMS model for human-computer interaction: International Journal of Human-Computer Interaction Vol 2(4) 1990, 287-305.
• Kato, Y., & Yamaguchi, S. (1992). A systematical study for psychological impression caused by fluctuating random noise based on fuzzy set theory: Journal of the Acoustical Society of America Vol 91(5) May 1992, 2748-2755.
• Katz, A., Vom Hau, M., & Mahoney, J. (2005). Explaining the Great Reversal in Spanish America: Fuzzy-Set Analysis Versus Regression Analysis: Sociological Methods & Research Vol 33(4) May 2005, 539-573.
• Katz, A., vom Hau, M., & Mahoney, J. (2005). Explaining the Great Reversal in Spanish America: Fuzzy-Set Analysis Versus Regression Analysis: Errata: Sociological Methods & Research Vol 34(1) Aug 2005, 142-143.
• Koustoumpardis, P. N., & Aspragathos, N. A. (2003). Fuzzy Logic Decision Mechanism Combined with a Neuro-Controller for Fabric Tension in Robotized Sewing Process: Journal of Intelligent & Robotic Systems Vol 36(1) Jan 2003, 65-88.
• Krishnapuram, R., & Lee, J. (1992). Fuzzy-set-based hierarchical networks for information fusion in computer vision: Neural Networks Vol 5(2) 1992, 335-350.
• Kronenfeld, B. J. (2003). Implications of a data reduction framework to assignment of fuzzy membership values in continuous class maps: Spatial Cognition and Computation Vol 3(2-3) 2003, 221-237.
• Kulkarni, A. D., & Cavanaugh, C. D. (2000). Fuzzy neural network models for classification: Applied Intelligence Vol 12(3) May 2000, 207-215.
• Kung, C.-Y. (2006). Using Fuzzy Sets and Grey Decision-Making to Construct the Performance Evaluation Model of Firm's Outsourcing Management - A Case Study of Avionics Manufacturer in Taiwan: Quality & Quantity: International Journal of Methodology Vol 40(4) Aug 2006, 577-593.
• Kuo, R. J., Hong, S. M., Lin, Y., & Huang, Y. C. (2008). Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules: Neurocomputing: An International Journal Vol 71(13-15) Aug 2008, 2893-2907.
• Kuo, R. J., Su, Y. T., Chiu, C. Y., Chen, K.-Y., & Tien, F. C. (2006). Part family formation through fuzzy ART2 neural network: Decision Support Systems Vol 42(1) Oct 2006, 89-103.
• Kuo, R. J., Wu, P., & Wang, C. P. (2002). An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination: Neural Networks Vol 15(7) Sep 2002, 909-925.
• Kurisu, K., & Fukuyama, K. (1996). Controlling public address systems based on fuzzy inference and neural network: Neurocomputing: An International Journal Vol 13(2-4) Oct 1996, 231-245.
• Lancieri, L., & Boubchir, L. (2007). Using multiple uncertain examples and adaptative fuzzy reasoning to optimize image characterization: Knowledge-Based Systems Vol 20(3) Apr 2007, 266-276.
• Lau, H. C. W., Hui, I. K., Chan, F. T. S., & Wong, C. W. Y. (2002). Monitoring the supply of products in a supply chain environment: A fuzzy neural approach: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 19(4) Sep 2002, 235-243.
• Lee, H.-M., Sheu, C.-C., & Chen, J.-M. (1998). Handwritten Chinese character recognition based on primitive and fuzzy features via the SEART neural net model: Applied Intelligence Vol 8(3) May-Jun 1998, 269-285.
• Lee, M.-R., Rhee, H., Cho, K., & Cho, B.-J. (2002). Self-tuning of the fuzzy interference rule by integrated method: Journal of Intelligent & Robotic Systems Vol 33(3) Mar 2002, 313-327.
• Lehto, M. R., House, T., & Papastavrou, J. D. (2000). Interpretation of fuzzy qualifiers by chemical workers: International Journal of Cognitive Ergonomics Vol 4(1) 2000, 73-86.
• Leung, R. W. K., Lau, H. C. W., & Kwong, C. K. (2004). An Intelligent System to Monitor the Chemical Concentration of Electroplating Process: An Integrated OLAP and Fuzzy Logic Approach: Artificial Intelligence Review Vol 21(2) Apr 2004, 139-159.
• Li, S., Davies, B., Edwards, J., Kinman, R., & Duan, Y. (2002). Integrating group Delphi, fuzzy logic and expert systems for marketing strategy development: The hybridisation and its effectiveness: Marketing Intelligence & Planning Vol 20(5) 2002, 273-284.
• Lin, C.-J., Liu, Y.-C., & Lee, C.-Y. (2008). Supervised and reinforcement evolutionary learning for wavelet-based neuro-fuzzy networks: Journal of Intelligent & Robotic Systems Vol 52(2) Jun 2008, 285-312.
• Lin, J.-W., Lee, S.-J., & Yang, H.-T. (2001). A stroke-based neuro-fuzzy system for handwritten Chinese character recognition: Applied Artificial Intelligence Vol 15(6) Jul 2001, 561-586.
• Lindsay, D. S., & Johnson, M. K. (2000). False memories and the source monitoring framework: Reply to Reyna and Lloyd (1997): Learning and Individual Differences Vol 12(2) Jun 2000, 145-161.
• Liu, F., Quek, C., & Ng, G. S. (2007). A novel generic hebbian ordering-based fuzzy rule base reduction approach to Mamdani neuro-fuzzy system: Neural Computation Vol 19(6) Jun 2007, 1656-1680.
• Liu, T. S., & Wu, J. C. (1993). A model for a rider-motorcycle system using fuzzy control: IEEE Transactions on Systems, Man, & Cybernetics Vol 23(1) Jan-Feb 1993, 267-276.
• Liu, Y., Liu, X., & Su, Z. (2008). A new fuzzy approach for handling class labels in canonical correlation analysis: Neurocomputing: An International Journal Vol 71(7-9) Mar 2008, 1735-1740.
• Looney, C. G. (2002). Radial basis functional link nets and fuzzy reasoning: Neurocomputing: An International Journal Vol 48 Oct 2002, 489-509.
• Luxhoj, J. T., & Hadjimichael, M. (2006). A hybrid fuzzy-belief network (HFBN) for modelling aviation safety risk factors: Human Factors and Aerospace Safety Vol 6(3) 2006, 191-215.
• Ma, M. (1990). Analysis of fuzzy statistics for designating value to qualifiers' semantics: Acta Psychologica Sinica Vol 22(1) 1990, 51-57.
• Madeira, M. J. (1990). The role of prototypes on fuzzy-set categorization in adults and children: PSICO Vol 20(2) Jul-Dec 1990, 45-67.
• Mamdani, E. H., & Assilian, S. (1999). An experiment in linguistic synthesis with a fuzzy logic controller: International Journal of Human-Computer Studies Vol 51(2) Aug 1999, 135-147.
• Mandal, D. P., Murthy, C. A., & Pal, S. K. (1992). Formulation of a multivalued recognition system: IEEE Transactions on Systems, Man, & Cybernetics Vol 22(4) Jul-Aug 1992, 607-620.
• Mann, G. K. I., & Gosine, R. G. (2002). Adaptive hierarchical tuning of fuzzy controllers: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 19(1) Feb 2002, 34-45.
• Massaro, D. W. (1998). Perceiving talking faces: From speech perception to a behavioral principle. Cambridge, MA: The MIT Press.
• Massaro, D. W., & Cohen, M. M. (1991). Integration versus interactive activation: The joint influence of stimulus and context in perception: Cognitive Psychology Vol 23(4) Oct 1991, 558-614.
• Massaro, D. W., & Cohen, M. M. (1999). Speech perception in perceivers with hearing loss: Synergy of multiple modalities: Journal of Speech, Language, and Hearing Research Vol 42(1) Feb 1999, 21-41.
• Massaro, D. W., & Cohen, M. M. (2000). Fuzzy logical model of bimodal emotion perception: Comment on "The perception of emotions by ear and by eye" by de Gelder and Vroomen: Cognition & Emotion Vol 14(3) May 2000, 313-320.
• Massaro, D. W., & Cohen, M. M. (2000). Tests of auditory-visual integration efficiency within the framework of the fuzzy logical model of perception: Journal of the Acoustical Society of America Vol 108(2) Aug 2000, 784-789.
• Massaro, D. W., Cohen, M. M., Campbell, C. S., & Rodriguez, T. (2001). Bayes factor of model selection validates FLMP: Psychonomic Bulletin & Review Vol 8(1) Mar 2001, 1-17.
• Matt, G. E., Turingan, M. R., Dinh, Q. T., Felsch, J. A., Hovell, M. F., & Gehrman, C. (2003). Improving self-reports of drug-use: Numeric estimates as fuzzy sets: Addiction Vol 98(9) Sep 2003, 1239-1247.
• May, E. C., Utts, J. M., Humphrey, B. S., Luke, W. L., & et al. (1990). Advances in remote-viewing analysis: Journal of Parapsychology Vol 54(3) Sep 1990, 193-228.
• Mazlack, L. J., & Lee, W. (1993). Identifying the most effective approximate reasoning calculi for a knowledge-based system: IEEE Transactions on Systems, Man, & Cybernetics Vol 23(5) Sep-Oct 1993, 1417-1424.
• Mbede, J. B., Wei, W., & Zhang, Q. (2001). Fuzzy and recurrent neural network motion control among dynamic obstacles for robot manipulators: Journal of Intelligent & Robotic Systems Vol 30(2) Feb 2001, 155-177.
• McCloskey, A., McIvor, R., Maguire, L., Humphreys, P., & O'Donnell, T. (2006). A user-centred corporate acquisition system: A dynamic fuzzy membership functions approach: Decision Support Systems Vol 42(1) Oct 2006, 162-185.

Application of hierarchical neural fuzzy models to modeling and control of a bioprocess: Applied Artificial Intelligence Vol 20(9) Oct 2006, 797-816.

• Meng, Q., & Li, Y. (1990). An experiment to introduce set-valued statistics into the evaluation method of the signal detectability theory: Acta Psychologica Sinica Vol 22(1) 1990, 16-22.
• Miao, C., Yang, Q., Fang, H., & Goh, A. (2007). A cognitive approach for agent-based personalized recommendation: Knowledge-Based Systems Vol 20(4) May 2007, 397-405.
• Miao, D., Hu, W., Dong, Y., & Chen, Y. (1997). The fuzzy set model of multistage evaluation degrees of positivity importance and its use: Psychological Science (China) Vol 20(6) 1997, 551-552, 524.
• Mili, H., & Rada, R. (1990). Inheritance generalized to fuzzy regularity: IEEE Transactions on Systems, Man, & Cybernetics Vol 20(5) Sep-Oct 1990, 1184-1198.
• Miller, P. H., & Bjorklund, D. F. (1998). Contemplating fuzzy-trace theory: The gist of it: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 184-193.
• Mitra, S., & Pal, S. K. (1994). Self-organizing neural network as a fuzzy classifier: IEEE Transactions on Systems, Man, & Cybernetics Vol 24(3) Mar 1994, 385-399.
• Molina, J. M., Herrero, J. G., Jimenez, F. J., & Casar, J. R. (2004). Fuzzy Reasoning in a Multiagent System of Surveillance Sensors to Manage Cooperatively the Sensor-to-Task Assignment Problem: Applied Artificial Intelligence Vol 18(8) Sep 2004, 673-711.
• Nanayakkara, D. P. T., Watanabe, K., Kiguchi, K., & Izumi, K. (2001). Evolutionary learning of a fuzzy behavior based controller for a nonholonomic mobile robot in a class of dynamic environments: Journal of Intelligent & Robotic Systems Vol 32(3) Nov 2001, 255-277.
• Neagu, C.-D., Avouris, N., Kalapanidas, E., & Palade, V. (2002). Neural and neuro-fuzzy integration in a knowledge-based system for air quality prediction: Applied Intelligence Vol 17(2) Sep 2002, 141-169.
• Nicole, S., & Caprara, G. V. (2005). Assessing Aggressive Behavior in Children: A Neuro-Fuzzy Model: European Journal of Psychological Assessment Vol 21(4) 2005, 255-264.
• Nishina, T., & Hagiwara, M. (1997). Fuzzy inference neural network: Neurocomputing: An International Journal Vol 14(3) Feb 1997, 223-239.
• Niu, B., Zhu, Y., He, X., & Shen, H. (2008). A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing: Neurocomputing: An International Journal Vol 71(7-9) Mar 2008, 1436-1448.
• No authorship, i. (1988). Review of Fuzzy Set Analysis for Behavioral and Social Sciences. Recent Research in Psychology: PsycCRITIQUES Vol 33 (1), Jan, 1988.
• Oden, G. C., & Lopes, L. L. (1997). Risky choice with fuzzy criteria: Psychologische Beitrage Vol 39(1-2) 1997, 56-82.
• Oh, S.-K., Pedrycz, W., & Park, H.-S. (2004). Rule-based multi-FNN identification with the aid of evolutionary fuzzy granulation: Knowledge-Based Systems Vol 17(1) Jan 2004, 1-13.
• Ohayon, M. M. (1999). Improving decisionmaking processes with the fuzzy logic approach in the epidemiology of sleep disorders: Journal of Psychosomatic Research Vol 47(4) Oct 1999, 297-311.
• Osherson, D., & Smith, E. E. (1997). On typicality and vagueness: Cognition Vol 64(2) Aug 1997, 189-206.
• Otero, A., Felix, P., Regueiro, C., Rodriguez, M., & Barro, S. (2006). Fuzzy constraint satisfaction approach for landmark recognition in mobile robotics: AI Communications Vol 19(3) 2006, 275-289.
• Papadimitriou, S., & Terzidis, K. (2005). Efficient and interpretable fuzzy classifiers from data with support vector learning: Intelligent Data Analysis Vol 9(6) 2005, 527-550.
• Parasuraman, R., Masalonis, A. J., & Hancock, P. A. (2000). Fuzzy signal detection theory: Basic postulates and formulas for analyzing human and machine performance: Human Factors Vol 42(4) Win 2000, 636-659.
• Park, D., Kandel, A., & Langholz, G. (1994). Genetic-based new fuzzy reasoning models with application to fuzzy control: IEEE Transactions on Systems, Man, & Cybernetics Vol 24(1) Jan 1994, 39-47.
• Park, J., & Han, S. H. (2004). A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design: International Journal of Industrial Ergonomics Vol 34(1) Jul 2004, 31-47.
• Parker, I. (2003). Lacanian social theory and clinical practice: Psychoanalysis & Contemporary Thought Vol 26(2) Spr 2003, 195-221.
• Parrado-Hernandez, E., Gomez-Sanchez, E., & Dimitriadis, Y. A. (2003). Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems: Neural Networks Vol 16(7) Sep 2003, 1039-1057.
• Pasquier, M., Quek, C., & Toh, M. (2001). Fuzzylot: A novel self-organising fuzzy-neural rule-based pilot system for automated vehicles: Neural Networks Vol 14(8) Oct 2001, 1099-1112.
• Paul, B., Konar, A., & Mandal, A. K. (1999). Fuzzy ADALINEs for gray image recognition: Neurocomputing: An International Journal Vol 24(1-3) Feb 1999, 207-223.
• Pedrycz, W. (1991). A referential scheme of fuzzy decision making and its neural network structure: IEEE Transactions on Systems, Man, & Cybernetics Vol 21(6) Nov-Dec 1991, 1593-1604.
• Peng, X. T., Kandel, A., & Wang, P. (1991). Concepts, rules, and fuzzy reasoning: A factor space approach: IEEE Transactions on Systems, Man, & Cybernetics Vol 21(1) Jan-Feb 1991, 194-205.
• Pertselakis, M., & Stafylopatis, A. (2008). Dynamic modular fuzzy neural classifier with tree-based structure identification: Neurocomputing: An International Journal Vol 71(4-6) Jan 2008, 801-812.
• Pinero, P., Garcia, P., Arco, L., Alvarez, A., Garcia, M. M., & Bonal, R. (2004). Sleep stage classification using fuzzy sets and machine learning techniques: Neurocomputing: An International Journal Vol 58-60 2004, 1137-1143.
• Placer, J., & Slobodchikoff, C. N. (2000). A fuzzy-neural system for identification of species-specific alarm calls of Gunnison's prairie dogs: Behavioural Processes Vol 52(1) Oct 2000, 1-9.
• Popp, H., & Lodel, D. (1995). Fuzzy techniques and user modeling in sales assistants: User Modeling and User-Adapted Interaction Vol 5(3-4) 1995, 349-370.
• Prade, H. (1990). A two-layer fuzzy pattern matching procedure for the evaluation of conditions involving vague quantifiers: Journal of Intelligent & Robotic Systems Vol 3(2) 1990, 93-101.
• Prieto, P., & San Luis, C. (1992). Application of fuzzy logic to psychological measurement: The Diffuse Graphic Scale as an alternative to response measurement: An empirical study: Psicologica Vol 13(3) 1992, 273-283.
• Qi, Z., McInroy, J. E., & Jafari, F. (2007). Trajectory tracking with parallel robots using low chattering, fuzzy sliding mode controller: Journal of Intelligent & Robotic Systems Vol 48(3) Mar 2007, 333-356.
• Qiu, G. F., Li, H. Z., Xu, L. D., & Zhang, W. X. (2003). A knowledge processing method for intelligent systems based on inclusion degree: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 20(4) Sep 2003, 187-195.
• Quinones, E. (2002). Historiography in the psychology of Antonio Caparros: An attempt to apply fuzzy logic and the small worlds theory to the study of scientific production: Anuario de Psicologia Vol 33(2) Jun 2002, 267-276.
• Ranganathan, R. S., Malki, H. A., & Chen, G. (2002). Fuzzy predictive PI control for processes with large time delays: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 19(1) Feb 2002, 21-33.
• Rasmani, K. A., & Shen, Q. (2006). Data-driven fuzzy rule generation and its application for student academic performance evaluation: Applied Intelligence Vol 25(3) Dec 2006, 305-319.
• Resche, C. (2004). Investigating 'Greenspanese': From hedging to 'fuzzy transparency': Discourse & Society Vol 15(6) Nov 2004, 723-744.
• Reyna, V. F. (2000). Fuzzy-trace theory and source monitoring: An evaluation of theory and false-memory data: Learning and Individual Differences Vol 12(2) Jun 2000, 163-175.
• Reyna, V. F. (2004). How people make decisions that involve risk: A dual-processes approach: Current Directions in Psychological Science Vol 13(2) Apr 2004, 60-66.
• Reyna, V. F., & Brainerd, C. J. (1992). A fuzzy-trace theory of reasoning and remembering: Paradoxes, patterns, and parallelism. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
• Reyna, V. F., & Brainerd, C. J. (1995). Fuzzy-trace theory: An interim synthesis: Learning and Individual Differences Vol 7(1) 1995, 1-75.
• Reyna, V. F., & Brainerd, C. J. (1998). Fuzzy-trace theory and false memory: New frontiers: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 194-209.
• Reyna, V. F., & Lloyd, F. (1997). Theories of false memory in children and adults: Learning and Individual Differences Vol 9(2) 1997, 95-123.
• Sanguanpong, S., & Kanlayasiri, U. (2007). Worm damage minimization in enterprise networks: International Journal of Human-Computer Studies Vol 65(1) Jan 2007, 3-16.
• Santamarina, C., & Salvendy, G. (1991). Fuzzy sets based knowledge systems and knowledge elicitation: Behaviour & Information Technology Vol 10(1) Jan-Feb 1991, 23-40.
• Sato, M., & Sato, Y. (1995). A general fuzzy clustering model based on aggregation operators: Behaviormetrika Vol 22(2) Jul 1995, 115-128.
• Scheltens, I., & van de Zee, E. (1996). The representation of word meaning: An evaluation of fuzzy set theory and conceptual semantics. North York, ON, England: Captus Press, Inc.
• Schooler, J. W. (1998). The distinctions of false and fuzzy memories: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 130-143.
• Selekwa, M. F., Dunlap, D. D., Shi, D., & Collins, E. G., Jr. (2008). Robot navigation in very cluttered environments by preference-based fuzzy behaviors: Robotics and Autonomous Systems Vol 56(3) Mar 2008, 231-246.
• Senay, H. (1992). Fuzzy command grammars for intelligent interface design: IEEE Transactions on Systems, Man, & Cybernetics Vol 22(5) Sep-Oct 1992, 1124-1131.
• Setnes, M., Babuska, R., & Verbruggen, H. B. (1998). Transparent fuzzy modelling: International Journal of Human-Computer Studies Vol 49(2) Aug 1998, 159-179.
• Shashi, M., Raju, K., & Avadhani, P. S. (1994). Reasoning with fuzzy censors: IEEE Transactions on Systems, Man, & Cybernetics Vol 24(7) Jul 1994, 1061-1064.
• Shaw, I. S. (1993). Fuzzy model of a human control operator in a compensatory tracking loop: International Journal of Man-Machine Studies Vol 39(2) Aug 1993, 305-332.
• Shie, J.-D., & Chen, S.-M. (2008). Feature subset selection based on fuzzy entropy measures for handling classification problems: Applied Intelligence Vol 28(1) Feb 2008, 69-82.
• Shiina, K. (1989). Osherson & Smith's paradox: Pitfalls in applying fuzzy set theory to psychology: Japanese Psychological Review Vol 32(4) 1989, 370-386.
• Shiu, S. C. K., Yeung, D. S., Sun, C. H., & Wang, X. Z. (2001). Transferring case knowledge to adaptation knowledge: An approach for case-base maintenance: Computational Intelligence Vol 17(2) May 2001, 295-314.
• Shmerko, V. P., & Yanushkevich, S. N. (2003). Three-Dimensional Feedforward Neural Networks and Their Realization by Nano-Devices: Artificial Intelligence Review Vol 20(3-4) Dec 2003, 473-494.
• Shouchui, Z., & Qiuiei, L. (2004). The Fuzzy-Trace Theory: A Challenge to Classical Cognitive Developmental Theories: Psychological Science (China) Vol 27(2) Mar 2004, 489-492.
• Smithson, M. (2005). Fuzzy Set Inclusion: Linking Fuzzy Set Methods With Mainstream Techniques: Sociological Methods & Research Vol 33(4) May 2005, 431-461.
• Soud, D., & Kazemian, H. B. (2004). A fuzzy approach to active usage parameter control in IEEE 802.11b wireless networks: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 21(5) Nov 2004, 269-278.
• Steenkamp, J.-B. E., & Wedel, M. (1991). Segmenting retail markets on store image using a consumer-based methodology: Journal of Retailing Vol 67(3) Fal 1991, 300-320.
• Stein, L. M., & Pergher, G. K. (2001). Creating false memories in adults using associated word lists: Psicologia: Reflexao e Critica Vol 14(2) 2001, 353-366.
• Steyaert, J. (1994). Soft computing for soft technologies: Artificial neural networks and fuzzy set theory for human services: Computers in Human Services Vol 10(4) 1994, 55-67.
• Sudkamp, T., & Hammell, R. J. (1994). Interpolation, completion, and learning fuzzy rules: IEEE Transactions on Systems, Man, & Cybernetics Vol 24(2) Feb 1994, 332-342.
• Symeonaki, M. A., Stamou, G. B., & Tzafestas, S. G. (2002). Fuzzy non-homogeneous Markov systems: Applied Intelligence Vol 17(2) Sep 2002, 203-214.
• Szu, H., Garcia, J., Zadeh, L., Hsu, C. C., DeWitte, J., Jr., Moon, G., et al. (1994). Neural network models for chaotic-fuzzy information processing. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
• Szu, H. H., DeWitte, J. T., Jr., & Crilly, B. C. (1995). Attempts to unify chaos, fuzzy logic and neural networks. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
• Takemura, K. (2007). Ambiguous comparative judgment: Fuzzy set model and data analysis: Japanese Psychological Research Vol 49(2) May 2007, 148-156.
• Tauchert, A. (2002). Fuzzy gender: Between female-embodiment and intersex: Journal of Gender Studies Vol 11(1) Mar 2002, 29-38.
• Tessem, B., & Davidsen, P. I. (1994). Fuzzy system dynamics: An approach to vague and qualitative variables in simulation: System Dynamics Review Vol 10(1) Spr 1994, 49-62.
• Tsourveloudis, N. C., & Valavanis, K. P. (2002). On the measurement of enterprise agility: Journal of Intelligent & Robotic Systems Vol 33(3) Mar 2002, 329-342.
• Tsuchiya, T. (1996). Scaling methods for qualitative three-mode data by partitioning items into G groups: Japanese Journal of Educational Psychology Vol 44(4) Dec 1996, 425-434.
• Turksen, I. B., & Tian, Y. (1995). Two-level tree search in fuzzy expert systems: IEEE Transactions on Systems, Man, & Cybernetics Vol 25(4) Apr 1995, 555-568.
• Turowski, K., & Weng, U. (2002). Representing and processing fuzzy information--An XML-based approach: Knowledge-Based Systems Vol 15(1-2) Jan 2002, 67-75.
• Tzafestas, S. G., & Rigatos, G. G. (1999). A simple robust sliding-mode fuzzy-logic controller of the diagonal type: Journal of Intelligent & Robotic Systems Vol 26(3-4) Nov-Dec 1999, 353-388.
• Ubeyli, E. D. (2007). Detection of electrocardiogram beats using a fuzzy similarity index: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 24(2) May 2007, 87-96.
• Ung, S. T., Williams, V., Bonsall, S., & Wang, J. (2006). Test case based risk predictions using artificial neural network: Journal of Safety Research Vol 37(3) 2006, 245-260.
• Vachkov, G., & Fukuda, T. (2001). Structured learning and decomposition of fuzzy models for robotic control applications: Journal of Intelligent & Robotic Systems Vol 32(1) Sep 2001, 1-21.
• van Geert, P. (2002). Developmental dynamics, intentional action, and fuzzy sets. New York, NY: Cambridge University Press.
• Verkuilen, J. (2005). Assigning Membership in a Fuzzy Set Analysis: Sociological Methods & Research Vol 33(4) May 2005, 462-496.
• Verkuilen, J. (2005). "Assigning Membership in a Fuzzy Set Analysis": Errata: Sociological Methods & Research Vol 34(2) Nov 2005, 259.
• Viswanathan, M., Bergen, M., Dutta, S., & Childres, T. (1996). Does a single response category in a scale completely capture a response? : Psychology & Marketing Vol 13(5) Aug 1996, 457-479.
• Viswanathan, M., & Childers, T. L. (1999). Understanding how product attributes influence product categorization: Development and validation of fuzzy set-based measures of gradedness in product categories: Journal of Marketing Research Vol 36(1) Feb 1999, 75-94.
• Wang, C.-H., Tsai, C.-J., Hong, T.-P., & Tseng, S.-S. (2003). Fuzzy inductive learning strategies: Applied Intelligence Vol 18(2) Mar-Apr 2003, 179-193.
• Wang, S.-M., Lai, L.-C., Wu, C.-J., & Shiue, Y.-L. (2007). Kinematic control of omni-directional robots for time-optimal movement between two configurations: Journal of Intelligent & Robotic Systems Vol 49(4) Aug 2007, 397-410.
• Wang, X.-Z., Dong, C.-R., & Fan, T.-G. (2007). Training T-S norm neural networks to refine weights for fuzzy if-then rules: Neurocomputing: An International Journal Vol 70(13-15) Aug 2007, 2581-2587.
• Watanabe, K., Hara, K., Koga, S., & Tzafestas, S. G. (1995). Fuzzy-neural network controllers using mean-value-based functional reasoning: Neurocomputing: An International Journal Vol 9(1) Sep 1995, 39-61.
• Wedel, M., & Steenkamp, J.-B. E. (1991). A clusterwise regression method for simultaneous fuzzy market structuring and benefit segmentation: Journal of Marketing Research Vol 28(4) Nov 1991, 385-396.
• Wenger, M. J., & Payne, D. G. (1997). Cue integration across study tasks and direct and indirect retrieval instructions: Implications for the study of retrieval processes: Journal of Experimental Psychology: Learning, Memory, and Cognition Vol 23(1) Jan 1997, 102-122.
• Wolfe, C. R. (1995). Information seeking on Bayesian conditional probability problems: A fuzzy-trace theory account: Journal of Behavioral Decision Making Vol 8(2) Jun 1995, 85-108.
• Wolfe, C. R. (2006). Cognitive technologies for gist processing: Behavior Research Methods Vol 38(2) May 2006, 183-189.
• Woodbury, M. A., & Manton, K. G. (1991). Empirical Bayes approaches to multivariate fuzzy partitions: Multivariate Behavioral Research Vol 26(2) Apr 1991, 291-321.
• Wright, D. B., & Loftus, E. F. (1998). How misinformation alters memories: Journal of Experimental Child Psychology Vol 71(2) Nov 1998, 155-164.
• Wu, C.-H., Yan, G.-L., & Lin, C.-L. (2002). Speech act modeling in a spoken dialog system using a fuzzy fragment-class Markov model: Speech Communication Vol 38(1-2) Sep 2002, 183-199.
• Wu, C.-j. (1994). A learning fuzzy algorithm for motion planning of mobile robots: Journal of Intelligent & Robotic Systems Vol 11(3) 1994-1995, 209-221.
• Wu, C.-J., & Lee, T.-L. (2004). A Fuzzy Mechanism for Action Selection of Soccer Robots: Journal of Intelligent & Robotic Systems Vol 39(1) Jan 2004, 57-70.
• Wu, J., & Rangaswamy, A. (2003). A Fuzzy Set Model of Search and Consideration with an Application to an Online Market: Marketing Science Vol 22(3) Sum 2003, 411-434.
• Wu, W.-Z., Zhang, W.-X., & Li, H.-Z. (2003). Knowledge acquisition in incomplete fuzzy information systems via the rough set approach: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 20(5) Nov 2003, 280-286.
• Xing, H.-J., & Hu, B.-G. (2008). An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification: Neurocomputing: An International Journal Vol 71(4-6) Jan 2008, 1008-1021.
• Yager, R. R. (1993). Element selection from a fuzzy subset using the fuzzy integral: IEEE Transactions on Systems, Man, & Cybernetics Vol 23(2) Mar-Apr 1993, 467-477.
• Yager, R. R. (1993). A general approach to criteria aggregation using fuzzy measures: International Journal of Man-Machine Studies Vol 39(2) Aug 1993, 187-213.
• Yager, R. R. (1994). Modeling and formulating fuzzy knowledge bases using neural networks: Neural Networks Vol 7(8) 1994, 1273-1283.
• Yamashita, K., & Yamashita, T. (1989). An attempt of fuzzy logic approach to development of logical judgment: Japanese Journal of Psychonomic Science Vol 8(1) Oct 1989, 33-38.
• Yamashita, T. (1988). A regression analysis approach to human judgments of fuzzy logic: Japanese Journal of Psychonomic Science Vol 7(1) Oct 1988, 1-7.
• Yamashita, T., & Yamashita, K. (1988). Fuzzy theory approach to concepts and logical judgment: Japanese Psychological Review Vol 31(2) 1988, 208-228.
• Yamashita, T., & Yamashita, K. (1989). Fuzzy logic for compound propositions: Japanese Journal of Psychology Vol 60(5) Dec 1989, 312-315.
• Yamashita, T., & Yamashita, K. (1989). Integration of fuzzy logical information in human judgments: Japanese Psychological Review Vol 32(4) 1989, 351-369.
• Yamashita, T., & Yamashita, K. (1992). Exemplifying and comparing fuzzy ratings and magnitude estimations: Psychologia: An International Journal of Psychology in the Orient Vol 35(4) Dec 1992, 240-248.
• Yao, Y. Y. (2003). Probabilistic approaches to rough sets: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 20(5) Nov 2003, 287-297.
• Yao, Y. Y., & Wong, S. K. (1992). A decision theoretic framework for approximating concepts: International Journal of Man-Machine Studies Vol 37(6) Dec 1992, 793-809.
• Yu, S.-C., & Yu, M.-N. (2007). Fuzzy partial credit scaling: A valid approach for scoring the Beck Depression Inventory: Social Behavior and Personality Vol 35(9) 2007, 1163-1172.
• Zeleznikow, J., & Nolan, J. R. (2001). Using soft computing to build real world intelligent decision support systems in uncertain domains: Decision Support Systems Vol 31(2) Jun 2001, 263-285.
• Zhang, Q. (1998). Fuzziness -- vagueness -- generality -- ambiguity: Journal of Pragmatics Vol 29(1) Jan 1998, 13-31.
• Zheng, Y.-j., Yang, J., Yang, J.-y., & Wu, X.-j. (2006). A reformative kernel Fisher discriminant algorithm and its application to face recognition: Neurocomputing: An International Journal Vol 69(13-15) Aug 2006, 1806-1810.
• Zhou, S., Wang, C., Wei, J., & Wu, S. (1999). Fuzzy segmentation spatiotemporal patterns of cognitive potential into microstates: Brain Topography Vol 12(1) Fal 1999, 61-67.
• Zhou, S., Wang, C., Wei, J., & Wu, S. (2000). Fuzzy segmentation spatiotemporal patterns of cognitive potential into microstates: Brain Topography Vol 12(3) Spr 2000, 241.
• Ziarko, W. (2003). Acquisition of hierarchy-structured probabilistic decision tables and rules from data: Expert Systems: International Journal of Knowledge Engineering and Neural Networks Vol 20(5) Nov 2003, 305-310.
• Zsigmond, I. (1999). Fuzzy-trace theory and the ability of selective encoding: Pszichologia: Az MTA Pszichologiai Intezetenek folyoirata Vol 19(4) 1999, 381-415.

### DissertationsEdit

• Dorsey, D. W. (1997). Managerial merit pay allocation: An analysis of alternative judgment models using regression and fuzzy expert system techniques. Dissertation Abstracts International: Section B: The Sciences and Engineering.
• Fulginiti, J. V. (1995). Ambiguity, precision, and choice: A fuzzy trace theory analysis of framing effects in decision-making under uncertainty. Dissertation Abstracts International: Section B: The Sciences and Engineering.
• Gogus, O. M. (1998). Fuzzy multi-criteria decision making. Dissertation Abstracts International: Section B: The Sciences and Engineering.
• Joo, Y. (1996). A fuzzy relational approach to cognitive structures of an urban knowledge-based system. Dissertation Abstracts International: Section B: The Sciences and Engineering.
• Juliano, B. J. A. (1993). A fuzzy logic approach for cognitive diagnosis: Dissertation Abstracts International.
• Kim, E. (1995). Analysis and meta-analysis of fuzzy relational structures by means of generalized morphism and their computer-aided tool support. Dissertation Abstracts International: Section B: The Sciences and Engineering.
• Laudeman, I. V. (1995). An application of unsupervised neural networks and fuzzy clustering in the identification of structure in personality data. Dissertation Abstracts International: Section B: The Sciences and Engineering.
• Laviolette, M. J. (1990). On the efficacy of fuzzy representations of uncertainty: Dissertation Abstracts International.
• Lotan, T. (1993). Modeling route choice behavior in the presence of information using concepts from fuzzy set theory and approximate reasoning: Dissertation Abstracts International.
• Macmillan, M. R. (1993). Measuring the effectiveness of different methods of user-based document clustering for information retrieval: A simulation: Dissertation Abstracts International.
• McDowell, B. D. (1997). Word recognition and the independent evaluation of letters. Dissertation Abstracts International: Section B: The Sciences and Engineering.
• Meleiro, L. A. C., Campello, R. J. G. B., Filho, R. M., & Amaral, W. C. (2006).
• Takayanagi, S. (1992). An examination of how researchers make statistical judgments: Statistical and fuzzy set approaches: Dissertation Abstracts International.
• Tang, J. (1997). Prototypes in Lesser Seal Scripts. Dissertation Abstracts International Section A: Humanities and Social Sciences.
• Wells, T. W. (1997). Fuzzy performance evaluation and risk assessment. Dissertation Abstracts International: Section B: The Sciences and Engineering.
• Willson, I. A. (1993). Fuzzy sets and uncertain choice processes: An empirical test of models: Dissertation Abstracts International.
• Yeung, F. Y. (2008). Developing a Partnering Performance Index (PPI) for construction projects---a Fuzzy Set Theory approach. Dissertation Abstracts International Section A: Humanities and Social Sciences.