Fuzzy system identification using an adaptive learning rule with terminal attractors

A fuzzy model with an adaptive learning rule is proposed to approximate a class of nonlinear systems. The proposed adaptive learning rule uses the concept of terminal attractors and guarantees that the identification is stable and fast convergence in finite time. Another subject of this paper is to determine the appropriate rule number of the fuzzy model required for a desired modeling error. A learning algorithm is also introduced to identify the centers of membership functions of fuzzy outputs.

[1]  Fuzzy Logic in Control Systems : Fuzzy Logic , 2022 .

[2]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. II , 1990, IEEE Trans. Syst. Man Cybern..

[3]  M. Chung,et al.  Identification of fuzzy relational model and its application to control , 1993 .

[4]  Chun-Yi Su,et al.  Adaptive control of a class of nonlinear systems with fuzzy logic , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[5]  Yong-Zai Lu,et al.  Fuzzy Model Identification and Self-Learning for Dynamic Systems , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  T. Johansen,et al.  Constructing NARMAX models using ARMAX models , 1993 .

[7]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[8]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .

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

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

[11]  Michail Zak,et al.  Terminal attractors in neural networks , 1989, Neural Networks.

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

[13]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

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

[15]  Y. Stepanenko,et al.  Adaptive control of a class of nonlinear systems with fuzzy logic , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[16]  Robert M. Sanner,et al.  Stable Recursive Identification Using Radial Basis Function Networks , 1992, 1992 American Control Conference.

[17]  Kazuo Tanaka,et al.  Successive identification of a fuzzy model and its applications to prediction of a complex system , 1991 .

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

[19]  James J. Buckley,et al.  Numerical relationships between neural networks, continuous functions, and fuzzy systems , 1993 .

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

[21]  James J. Buckley,et al.  Hybrid neural nets can be fuzzy controllers and fuzzy expert systems , 1993 .