A Method of Converting a Fuzzy System to a Two-Layered Hierarchical Fuzzy System and Its Run-Time Efficiency

In classical single-layer fuzzy systems (FSs), the number of rules and the run-time computational requirements increase exponentially with the number of input domains. In this paper, we present a method for converting a multidimensional FS to a two-layer hierarchical FS that reduces the number of rules and improves the run-time efficiency. The first layer of the two-layer system consists of FSs whose rule bases can be represented as linearly independent vectors. The second layer constructs linear combinations of the rule base vectors. The effectiveness of the hierarchical conversion in reducing the number of rules and improving efficiency is demonstrated on a classic control problem and a simulated higher dimensional FS.

[1]  Jin S. Lee,et al.  Universal approximation by hierarchical fuzzy system with constraints on the fuzzy rule , 2002, Fuzzy Sets Syst..

[2]  Jin S. Lee,et al.  A class of hierarchical fuzzy systems with constraints on the fuzzy rules , 2005, IEEE Transactions on Fuzzy Systems.

[3]  Carles Sierra,et al.  A knowledge level analysis of taxonomic domains , 1997 .

[4]  Valerie V. Cross,et al.  Patterns of fuzzy rule-based inference , 1994, Int. J. Approx. Reason..

[5]  Gi J. Jeon,et al.  An index of applicability for the decomposition method of multivariable fuzzy systems , 1995, IEEE Trans. Fuzzy Syst..

[6]  Francisco Herrera,et al.  Linguistic modeling by hierarchical systems of linguistic rules , 2002, IEEE Trans. Fuzzy Syst..

[7]  João Paulo Carvalho,et al.  Two-Input Fuzzy TPE Systems , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[8]  W. Brockmann,et al.  Function approximation with decomposed fuzzy systems , 1999, Fuzzy Sets Syst..

[9]  Jun Zhou,et al.  Hierarchical fuzzy control , 1991 .

[10]  Silverio Bolognani,et al.  Hardware and software effective configurations for multi-input fuzzy logic controllers , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[11]  Robert J. Hammell,et al.  Interpolation, Completion, and Learning Fuzzy Rules , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[12]  P. Kokotovic,et al.  Nonlinear control via approximate input-output linearization: the ball and beam example , 1992 .

[13]  Alexander E. Gegov Multilayer fuzzy control of multivariable systems by direct decomposition , 1998, Int. J. Syst. Sci..

[14]  Tai-Ming Parng,et al.  A new approach of multi-stage fuzzy logic inference , 1996, Fuzzy Sets Syst..

[15]  Kaoru Hirota,et al.  Parallel and Multistage Fuzzy Inference Based on Families of alpha-level sets , 1998, Inf. Sci..

[16]  Ronald R. Yager,et al.  On the construction of hierarchical fuzzy systems models , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[17]  Xiao-Jun Zeng,et al.  Decomposition property of fuzzy systems and its applications , 1996, IEEE Trans. Fuzzy Syst..

[18]  D. Linkens,et al.  A hierarchical multivariable fuzzy controller for learning with genetic algorithms , 1996 .

[19]  T. Fukuda,et al.  Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm , 1995 .

[20]  Li-Xin Wang,et al.  Universal approximation by hierarchical fuzzy systems , 1998, Fuzzy Sets Syst..

[21]  Xiao-Jun Zeng,et al.  Approximation Capabilities of Hierarchical Fuzzy Systems , 2005, IEEE Transactions on Fuzzy Systems.

[22]  Madan Gupta,et al.  Multivariable Structure of Fuzzy Control Systems , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  Francisco Herrera,et al.  A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[24]  Jun Zhou,et al.  Adaptive hierarchical fuzzy controller , 1993, IEEE Trans. Syst. Man Cybern..

[25]  Korris Fu-Lai Chung,et al.  On multistage fuzzy neural network modeling , 2000, IEEE Trans. Fuzzy Syst..