A new approach for fuzzy neural network weight initialization

We develop a method for extracting a fuzzy model directly from input-output data. Our approach is based on three fundamental factors: (1) The use of entropy theory for feature selection, (2) the identification of the fuzzy model structure in one single step by the incremental applying of the fuzzy-c-means algorithm directly to the Cartesian input-output data space, (3) the introduction of a new method "semi-Lambda-cut-density" based on the /spl lambda/-cut concept, for setting the initial weights in neurofuzzy networks (NFN). The NFN is trained by a backpropagation algorithm. A comparative study on benchmark examples is conducted and shows that our method solves the trade-off between the use of a small number of rules and the achievement of a fuzzy model best performance index.

[1]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

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

[3]  Yoshiki Uchikawa,et al.  An acquisition of operator's rules for collision avoidance using fuzzy neural networks , 1995, IEEE Trans. Fuzzy Syst..

[4]  Bart Kosko,et al.  Neural networks and fuzzy systems , 1998 .

[5]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[6]  Andrew A. Goldenberg,et al.  Development of a systematic methodology of fuzzy logic modeling , 1998, IEEE Trans. Fuzzy Syst..

[7]  Hisao Ishibuchi,et al.  Tradeoff between the performance of fuzzy rule-based classification systems and the number of fuzzy if-then rules , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[8]  Yinghua Lin,et al.  A new approach to fuzzy-neural system modeling , 1995, IEEE Trans. Fuzzy Syst..

[9]  Allan Tucker,et al.  Soft computing for intelligent data analysis , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[10]  Kazuo Tanaka,et al.  Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique , 1995, IEEE Trans. Fuzzy Syst..

[11]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[12]  Dennis Gabor,et al.  Theory of communication , 1946 .

[13]  Chin-Teng Lin,et al.  A neural fuzzy system with linguistic teaching signals , 1995, IEEE Trans. Fuzzy Syst..

[14]  Giovanna Castellano,et al.  An approach to structure identification of fuzzy models , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[15]  Euntai Kim,et al.  A new approach to fuzzy modeling , 1997, IEEE Trans. Fuzzy Syst..

[16]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

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

[18]  Isao Hayashi,et al.  Construction of fuzzy inference rules by NDF and NDFL , 1992, Int. J. Approx. Reason..

[19]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[20]  Sun-Yuan Kung,et al.  Digital neural networks , 1993, Prentice Hall Information and System Sciences Series.

[21]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[22]  Y. Fukuyama,et al.  A new method of choosing the number of clusters for the fuzzy c-mean method , 1989 .

[23]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

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

[25]  Yaochu Jin,et al.  An approach to rule-based knowledge extraction , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[26]  Marco Russo Comments on "A new approach to fuzzy-neural system modeling" [and reply] , 1996, IEEE Trans. Fuzzy Syst..