Fuzzy rule extraction from a trained multilayer neural network

This paper focuses on one half of the knowledge acquisition problem for fuzzy systems, namely the acquisition of a fuzzy rule base from a set of input/output data, and in particular on the extraction of a set of fuzzy rules from a trained neural network. Some limitations with previously reported work in this area are first identified. Two simple rule extraction techniques are then described and tested on a well known classification problem. The performance of the resultant rule bases compares more favourably than those reported using alternative techniques.

[1]  Nikola Kasabov,et al.  Learning Fuzzy Production Rules For Approximate Reasoning In Connectionist Production Systems , 1993 .

[2]  Richard Weber,et al.  Fuzzy-ID3: A class of methods for automatic knowledge acquisition , 1992 .

[3]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[4]  LiMin Fu,et al.  Rule Generation from Neural Networks , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[5]  H. Ishibuchi,et al.  Selecting fuzzy rules by genetic algorithm for classification problems , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[6]  Chuen-Tsai Sun,et al.  A neuro-fuzzy classifier and its applications , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[7]  Naoki Hara,et al.  Fuzzy rule extraction from a multilayered neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

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

[9]  Elisabetta Binaghi,et al.  Empirical learning for fuzzy knowledge acquisition , 1992 .

[10]  Nikola K. Kasabov,et al.  Learning fuzzy rules through neural networks , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[11]  P.E. Maher,et al.  Uncertain reasoning in an ID3 machine learning framework , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.