Nonlinear System Modeling with a New Fuzzy Model and Neural Compensation

Normal fusion procedure of neural networks and fuzzy systems is to use neural learning techniques to train membership functions of fuzzy system. If no mechanistic prior knowledge can be used, fuzzy systems should be obtained from a set of data. The same data are usually used to train these fuzzy systems in the framework of fuzzy neural networks. But modeling accuracy cannot be improved extraordinary, because neural training and fuzzy modeling use the same data set. In this paper, we propose a new modeling idea, the fuzzy system will not be changed, and modeling error between real plant and the fuzzy system is compensated by a neural network. Another contribution of this paper is fuzzy model is generated automatically by kernel smoothing technique. The third contribution of this paper is a new learning approach for neural compensator is proposed, which assures stable and faster learning.

[1]  Johan A. K. Suykens,et al.  NLq theory: checking and imposing stability of recurrent neural networks for nonlinear modeling , 1997, IEEE Trans. Signal Process..

[2]  Léon Personnaz,et al.  Neural-network construction and selection in nonlinear modeling , 2003, IEEE Trans. Neural Networks.

[3]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[4]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[5]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control , 1994 .

[6]  John Moody,et al.  Learning rate schedules for faster stochastic gradient search , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.

[7]  Sheng Chen,et al.  Recursive hybrid algorithm for non-linear system identification using radial basis function networks , 1992 .

[8]  Lee A. Feldkamp,et al.  Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.

[9]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[10]  Jacek M. Leski,et al.  TSK-fuzzy modeling based on /spl epsiv/-insensitive learning , 2005, IEEE Transactions on Fuzzy Systems.

[11]  Jung-Hsien Chiang,et al.  Support vector learning mechanism for fuzzy rule-based modeling: a new approach , 2004, IEEE Trans. Fuzzy Syst..

[12]  E. Nadaraya.  On Estimating Regression , 1964 .

[13]  M. Priestley,et al.  Non‐Parametric Function Fitting , 1972 .

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

[15]  Kumpati S. Narendra,et al.  Adaptive control of discrete-time systems using multiple models , 2000, IEEE Trans. Autom. Control..

[16]  Wen Yu,et al.  Fuzzy identification using fuzzy neural networks with stable learning algorithms , 2004 .

[17]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[18]  Ieroham S. Baruch,et al.  A fuzzy-neural multi-model for nonlinear systems identification and control , 2008, Fuzzy Sets Syst..

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