Generalised influential rule search scheme for fuzzy function approximation

The present paper is a humble attempt to develop a fuzzy function approximator which can completely self-generate its fuzzy rule base and input-output membership functions from an input-output data set. The fuzzy system can be further adapted to modify its rule base and output membership functions to provide satisfactory performance. This proposed scheme, called generalised influential rule search scheme, has been successfully implemented to develop pure fuzzy function approximators as well as fuzzy logic controllers. The satisfactory performance of the proposed scheme is amply demonstrated by implementing it to develop different major components in a process control loop. The versatility of the algorithm is further proved by implementing it for a benchmark nonlinear function approximation problem.

[1]  Patrick Siarry,et al.  Fuzzy rule base learning through simulated annealing , 1999, Fuzzy Sets Syst..

[2]  Tzung-Pei Hong,et al.  Finding relevant attributes and membership functions , 1999, Fuzzy Sets Syst..

[3]  Roberto Kawakami Harrop Galvão,et al.  Extracting fuzzy control rules from experimental human operator data , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Tzung-Pei Hong,et al.  Processing individual fuzzy attributes for fuzzy rule induction , 2000, Fuzzy Sets Syst..

[5]  Héctor Pomares,et al.  A systematic approach to a self-generating fuzzy rule-table for function approximation , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Héctor Pomares,et al.  Self-organized fuzzy system generation from training examples , 2000, IEEE Trans. Fuzzy Syst..

[7]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[8]  Kwang Bo Cho,et al.  Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction , 1996, Fuzzy Sets Syst..

[9]  Xiao-Jun Zeng,et al.  Approximation accuracy analysis of fuzzy systems as function approximators , 1996, IEEE Trans. Fuzzy Syst..

[10]  Tzung-Pei Hong,et al.  Induction of fuzzy rules and membership functions from training examples , 1996, Fuzzy Sets Syst..

[11]  Marco Russo,et al.  FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling , 1998, IEEE Trans. Fuzzy Syst..

[12]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[13]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[14]  Hideo Tanaka,et al.  Empirical study on learning in fuzzy systems , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[15]  Patrick Siarry,et al.  A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases , 1998, Fuzzy Sets Syst..

[16]  Ahmad Lotfi,et al.  Learning fuzzy inference systems using an adaptive membership function scheme , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[18]  Hisao Ishibuchi,et al.  A simple but powerful heuristic method for generating fuzzy rules from numerical data , 1997, Fuzzy Sets Syst..

[19]  Ching-Chang Wong,et al.  A self-generating method for fuzzy system design , 1999, Fuzzy Sets Syst..

[20]  Euntai Kim,et al.  A new approach to fuzzy modeling , 1997, IEEE Trans. Fuzzy Syst..

[21]  James M. Keller,et al.  Neural network implementation of fuzzy logic , 1992 .

[22]  P. Siarry,et al.  A centered formulation of Takagi-Sugeno rules for improved learning efficiency , 1994 .

[23]  Frank Klawonn,et al.  Constructing a fuzzy controller from data , 1997, Fuzzy Sets Syst..

[24]  Haralambos Sarimveis,et al.  A hierarchical fuzzy-clustering approach to fuzzy modeling , 2005, Fuzzy Sets Syst..

[25]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[26]  Roberto Guerrieri,et al.  Fuzzy sets of rules for system identification , 1996, IEEE Trans. Fuzzy Syst..

[27]  Amitava Chatterjee,et al.  Influential rule search scheme (IRSS) - a new fuzzy pattern classifier , 2004, IEEE Transactions on Knowledge and Data Engineering.

[28]  Shigeo Abe,et al.  Fuzzy rules extraction directly from numerical data for function approximation , 1995, IEEE Trans. Syst. Man Cybern..

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

[30]  Rudolf Kruse,et al.  Neuro-fuzzy systems for function approximation , 1999, Fuzzy Sets Syst..

[31]  Shigeo Abe,et al.  Function approximation based on fuzzy rules extracted from partitioned numerical data , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[32]  Reza Langari,et al.  Fuzzy models, modular networks, and hybrid learning , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[33]  H Tanaka,et al.  A SIMPLE BUT POWERFUL METHOD FOR GENERATING FUZZY RULES FROM NUMERICAL DATA , 1997 .

[34]  P. K. Simpson.  Fuzzy Min-Max Neural Networks-Part 1 : Classification , 1992 .

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

[36]  Shyi-Ming Chen,et al.  A new method for constructing membership functions and fuzzy rules from training examples , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[37]  Euntai Kim,et al.  A Simple Identified Sugeno-Type Fuzzy Model via Double Clustering , 1998, Inf. Sci..

[38]  Rajani K. Mudi,et al.  A robust self-tuning scheme for PI- and PD-type fuzzy controllers , 1999, IEEE Trans. Fuzzy Syst..

[39]  Amitava Chatterjee,et al.  A new adaptive fuzzy controller with saturation employing influential rule search scheme (IRSS) , 2007, Int. J. Knowl. Based Intell. Eng. Syst..