Evolutionary Design of Neuro-Fuzzy Systems - A Generic Framework

A Neuro-Fuzzy (NF) system is a combination of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS) in such a way that ANN learning algorithms are used to determine the parameters of FIS. Potential interactions between connectionist learning systems and evolutionary search procedures have attracted lots of research work recently. There is no guarantee that the learning algorithm converges in ANN and the tuning of FIS will be successful. Success of evolutionary search procedures for optimal design of ANNs and FIS are well proven and established in many application areas. However, the evolutionary design of a NF system is yet to be explored. In this paper, we attempt to formulate a 5-tier hierarchical evolutionary search procedure for the optimal design of NF systems. We formulate each of the evolutionary search procedures in detail and the interactions among them.

[1]  Okyay Kaynak,et al.  Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications , 1998, NATO ASI Series.

[2]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.

[3]  Ajith Abraham,et al.  Optimal Design of Neural Nets Using Hybrid Algorithms , 2000, PRICAI.

[4]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[5]  Ajith Abraham,et al.  Evolutionary Design of Fuzzy Control Systems A Hybrid Approach , 2000 .

[6]  Jacek M. Zurada,et al.  Computational Intelligence: Imitating Life , 1994 .

[7]  Detlef Nauck,et al.  Foundations Of Neuro-Fuzzy Systems , 1997 .

[8]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[9]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[10]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[11]  Shun'ichi Tano,et al.  Deep combination of fuzzy inference and neural network in fuzzy inference software - FINEST , 1996, Fuzzy Sets Syst..

[12]  菅野 道夫,et al.  Industrial applications of fuzzy control , 1985 .

[13]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[14]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.