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An integrated method for the construction of compact fuzzy neural models

Lookup NU author(s): Dr Wanqing ZhaoORCiD


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To construct a compact fuzzy neural model with an appropriate number of inputs and rules is still a challenging problem. To reduce the number of basis vectors most existing methods select significant terms from the rule consequents, regardless of the structure and parameters in the premise. In this paper, a new integrated method for structure selection and parameter learning algorithm is proposed. The selection takes into account both the premise and consequent structures, thereby achieving simultaneously a more effective reduction in local model inputs relating to each rule, the total number of fuzzy rules, and the whole network inputs. Simulation results are presented which confirm the efficacy and superiority of the proposed method over some existing approaches. © 2010 Springer-Verlag Berlin Heidelberg.

Publication metadata

Author(s): Zhao W, Li K, Irwin GW, Fei M

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing (ICIC 2010)

Year of Conference: 2010

Pages: 102-109

ISSN: 0302-9743

Publisher: Springer


DOI: 10.1007/978-3-642-14922-1_14

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783642149214