Compensatory neurofuzzy systems with fast learning algorithms

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of a cart-pole balancing system and nonlinear system modeling have shown that: 1) the compensatory neurofuzzy system can effectively learn commonly used fuzzy IF-THEN rules from either well-defined initial data or ill-defined data; 2) the convergence speed of the compensatory learning algorithm is faster than that of the conventional backpropagation algorithm; and 3) the efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree.

[1]  James J. Buckley,et al.  Fuzzy neural network with fuzzy signals and weights , 1993, Int. J. Intell. Syst..

[2]  Kevin D. Reilly,et al.  Genetic learning algorithms for fuzzy neural nets , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[3]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[4]  Abraham Kandel,et al.  Hybrid decision-making system for fuzzy moves , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[5]  Paul J. Werbos,et al.  Neurocontrol and fuzzy logic: Connections and designs , 1992, Int. J. Approx. Reason..

[6]  H. Zimmermann,et al.  Latent connectives in human decision making , 1980 .

[7]  James C. Bezdek,et al.  Fuzzy Kohonen clustering networks , 1994, Pattern Recognit..

[8]  Abraham Kandel,et al.  Fuzzy Switching and Automata: Theory and Applications , 1979 .

[9]  Spyros G. Tzafestas,et al.  Neural fuzzy control systems with structure and parameter learning , 1996, J. Intell. Robotic Syst..

[10]  Edward T. Lee,et al.  Fuzzy Sets and Neural Networks , 1974 .

[11]  Madan M. Gupta,et al.  On the principles of fuzzy neural networks , 1994 .

[12]  Hisao Ishibuchi,et al.  Neural networks that learn from fuzzy if-then rules , 1993, IEEE Trans. Fuzzy Syst..

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

[14]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[15]  Chin-Teng Lin,et al.  Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[16]  Kazuo Asakawa,et al.  Neural networks in Japan , 1994, CACM.

[17]  Y. Kuo,et al.  A fuzzy neural network model with three-layered structure , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

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

[19]  Chin-Teng Lin,et al.  Real-time supervised structure/parameter learning for fuzzy neural network , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[20]  Witold Pedrycz,et al.  Fuzzy sets engineering , 1995 .

[21]  Rudolf Kruse,et al.  A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation , 1993, IEEE International Conference on Neural Networks.

[22]  Paul J. Werbos,et al.  The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting , 1994 .

[23]  H. Ishigami,et al.  Structure optimization of fuzzy neural network by genetic algorithm , 1995 .

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

[25]  Takeshi Yamakawa,et al.  A design algorithm of membership functions for a fuzzy neuron using example-based learning , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[26]  R. Katayama,et al.  Self generating radial basis function as neuro-fuzzy model and its application to nonlinear prediction of chaotic time series , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[27]  Chin-Teng Lin,et al.  A neural fuzzy control system with structure and parameter learning , 1995 .

[28]  C. S. George Lee,et al.  Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems , 1994, IEEE Trans. Fuzzy Syst..

[29]  Edward T. Lee,et al.  Fuzzy Neural Networks , 1975 .

[30]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.