Fuzzy models, modular networks, and hybrid learning

This paper proposes a new approach that can integrate fuzzy logic and neural networks in a "natural" manner. Unlike most existing fuzzy-neural models which usually makes use of the structure of feedforward multilayer networks, the proposed model takes advantage of the structure of a kind of modular networks. We show that fuzzy models have a direct correspondence with the modular networks. Based on this correspondence, we develop an efficient hybrid learning scheme which combines an unsupervised learning algorithm (fuzzy-c-means algorithm) and a supervised algorithm (LMS algorithm). The utility of the proposed approach is illustrated using the well-known Zimmermann and Zysno data.<<ETX>>

[1]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[2]  Reza Langari,et al.  Synthesis of Nonlinear Control Strategies via Fuzzy Logic , 1993, 1993 American Control Conference.

[3]  Ronald R. Yager,et al.  Implementing fuzzy logic controllers using a neural network framework , 1992 .

[4]  Joonwhoan Lee,et al.  Fuzzy-connective-based hierarchical aggregation networks for decision making , 1992 .

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

[6]  Michael I. Jordan,et al.  Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks , 1990, Cogn. Sci..

[7]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[8]  M. Sugeno,et al.  Fuzzy modeling and control of multilayer incinerator , 1986 .

[9]  W. Pedrycz,et al.  Generalized means as model of compensative connectives , 1984 .

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

[11]  Reza Langari,et al.  Fuzzy systems with competitive objectives , 1995, Proceedings of 1995 American Control Conference - ACC'95.

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

[13]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[14]  Michael I. Jordan,et al.  A Competitive Modular Connectionist Architecture , 1990, NIPS.

[15]  Dimitar Filev,et al.  Unified structure and parameter identification of fuzzy models , 1993, IEEE Trans. Syst. Man Cybern..

[16]  J. Buckley.  Sugeno type controllers are universal controllers , 1993 .

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

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

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

[20]  H. Takagi.  Cooperative systems of neural networks and fuzzy logic and its applications , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[21]  Gaetan Libert,et al.  Combining pattern recognition techniques with Akaike's information criteria for identifying ARMA models , 1994, IEEE Trans. Signal Process..

[22]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[23]  Reza Langari,et al.  Complex systems modeling via fuzzy logic , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[24]  M. Sugeno,et al.  Multi-dimensional fuzzy reasoning , 1983 .

[25]  J. Liao,et al.  A method for automatic generation of a fuzzy model , 1993, Third International Conference on Industrial Fuzzy Control and Intelligent Systems.

[26]  John S. Bridle,et al.  Training Stochastic Model Recognition Algorithms as Networks can Lead to Maximum Mutual Information Estimation of Parameters , 1989, NIPS.

[27]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[28]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[29]  Isao Hayashi,et al.  NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..

[30]  Reza Langari,et al.  Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques , 1995, IEEE Trans. Fuzzy Syst..

[31]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .