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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.
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
URL: https://doi.org/10.1007/978-3-642-14922-1_14
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