A design methodology for fuzzy system interfaces

Conceptually, a fuzzy system interacting with a numerical environment has three components: a numeric/linguistic interface, a linguistic processing unit, and a linguistic/numeric interface. At these interfaces, membership functions representing linguistic terms play a top role both for the linguistic meaning provided and for the pre/post information processing introduced to the fuzzy system. Considering these issues, a set of membership function properties is postulated. Furthermore, an expert-free interface design methodology able to meet these properties, and based on the concept of optimal interfaces, is proposed. This concept simply states an equivalence between information format (numeric and linguistic), thereby making the methodology appealing from the applicational point of view. An algorithm is developed, and brief notes on selected applications are outlined stressing relevant issues of the proposed methodology.

[1]  Lotfi A. Zadeh,et al.  The Calculus of Fuzzy If/Then Rules , 1992, Fuzzy Days.

[2]  João M. Lemos,et al.  Long-range predictive adaptive fuzzy relational control , 1995 .

[3]  L. Zadeh.  A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[4]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[5]  W. Pedrycz.  Processing in relational structures: fuzzy relational equations , 1991 .

[6]  D. Dubois,et al.  Fuzzy sets, probability and measurement , 1989 .

[7]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[8]  B. Kosko.  Fuzziness vs. probability , 1990 .

[9]  Peter J. Rousseeuw,et al.  Fuzzy clustering algorithms based on the maximum likelihood principle , 1991 .

[10]  Luís B. Almeida,et al.  Speeding up Backpropagation , 1990 .

[11]  R. Yager,et al.  On the issue of defuzzification and selection based on a fuzzy set , 1993 .

[12]  T. Runkler,et al.  A set of axioms for defuzzification strategies towards a theory of rational defuzzification operators , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[13]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[14]  Witold Pedrycz,et al.  Fuzzy neural networks and neurocomputations , 1993 .

[15]  W. Pedrycz,et al.  Fuzzy relation equations theory as a basis of fuzzy modelling: an overview , 1991 .

[16]  E. Cox,et al.  Fuzzy fundamentals , 1992, IEEE Spectrum.

[17]  J. V. Oliveira,et al.  Neuron inspired learning rules for fuzzy relational structures , 1993 .

[18]  Dimitar P. Filev,et al.  A generalized defuzzification method via bad distributions , 1991, Int. J. Intell. Syst..

[19]  W. Pedrycz.  An identification algorithm in fuzzy relational systems , 1984 .

[20]  Michio Sugeno,et al.  An introductory survey of fuzzy control , 1985, Inf. Sci..

[21]  Witold Pedrycz.  Identification in fuzzy systems , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[22]  S. Mabuchi.  A proposal for a defuzzification strategy by the concept of sensitivity analysis , 1993 .

[23]  I. Turksen,et al.  A model for the measurement of membership and the consequences of its empirical implementation , 1984 .

[24]  J. V. de Oliveira,et al.  On optimal fuzzy systems I/O interfaces , 1993 .

[25]  Lotfi A. Zadeh,et al.  Fuzzy logic and the calculus of fuzzy if-then rules , 1992, [1992] Proceedings The Twenty-Second International Symposium on Multiple-Valued Logic.

[26]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[27]  I. Turksen.  Measurement of membership functions and their acquisition , 1991 .

[28]  D. Willaeys,et al.  The use of fuzzy sets for the treatment of fuzzy information by computer , 1981 .

[29]  I. Turksen,et al.  Constraints on membership functions of rules in fuzzy expert systems , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.