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Rule extraction using a novel class of fuzzy degraded hyperellipsoidal composite neural networks

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4 Author(s)
Mu-Chun Su ; Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan ; Chien-Jen Kao ; Kai-Ming Liu ; Chi-Yeh Liu

Presents an innovative approach to rule extraction directly from experimental numerical data for system identification. The authors discuss how to use a novel class of fuzzy degraded hyperellipsoidal composite neural networks (FDHECNN's) to extract fuzzy if-then rules. The fuzzy rules are defined by hyperellipsoids of which principal axes are parallel to the coordinates of the input space. These rules are extracted from the parameters of the trained FDHECNN's. Based on a special learning scheme, the FDHECNN's can evolve automatically to acquire a set of fuzzy rules for approximating the input/output functions considered systems. A highly nonlinear system is used to test the proposed neuro-fuzzy systems

Published in:

Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int  (Volume:1 )

Date of Conference:

20-24 Mar 1995