A highly interpretable form of Sugeno inference systems

We present a form of fuzzy inference systems (FISs) that is highly interpretable and easy to manipulate. The form is based on a judicious choice of membership functions that have strong locality and differentiability properties and on a modification of the Sugeno and generalized Sugeno forms of the consequent polynomials so as to make them rule centered. Under these conditions, the coefficients in the consequent polynomials can be exactly interpreted as Taylor series coefficients. Besides the intuitive interpretation thus bestowed on the coefficients, we show that the new form allows easy design, manipulation, testing, training, and combination of the resulting fuzzy inference systems. The rudiments of a calculus of fuzzy inference systems are then introduced.

[1]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[2]  J. Buckley,et al.  Fuzzy hierarchical analysis , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[3]  Abdollah Homaifar,et al.  Full design of fuzzy controllers using genetic algorithms , 1992, Optics & Photonics.

[4]  H. Nomura,et al.  A Self-Tuning Method of Fuzzy Reasoning By Genetic Algorithm , 1993 .

[5]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[6]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[7]  J. Buckley Sugeno type controllers are universal controllers , 1993 .

[8]  Abdollah Homaifar,et al.  Fuzzy Inference for Variable Structure Control , 1994, J. Intell. Fuzzy Syst..

[9]  Abdollah Homaifar,et al.  Learning based approach to the design of hierarchical hybrid fuzzy PID controllers , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[10]  Abdollah Homaifar,et al.  Hierarchical Learning-Based Design of a Hybrid Fuzzy Pid Controller , 1997, Intell. Autom. Soft Comput..

[11]  Tsu-Tian Lee,et al.  Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[12]  Erich Peter Klement,et al.  Interpolation and Approximation of Real Input-Output Functions Using Fuzzy Rule Bases , 1994 .

[13]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[15]  Abdollah Homaifar,et al.  Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[16]  H. Takagi,et al.  Integrating Design Stages of Fuzzy Systems using Genetic Algorithms 1 , 1993 .

[17]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[18]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[19]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[20]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[21]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[22]  T. Fukuda,et al.  Self-tuning fuzzy inference based on spline function , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[23]  Stephen H. Lane,et al.  Multi-Layer Perceptrons with B-Spline Receptive Field Functions , 1990, NIPS.

[24]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.