An integrated method for the construction of compact fuzzy neural models

Zhao, Wanqing ORCID: https://orcid.org/0000-0001-6160-9547, Li, Kang, Irwin, George W. and Fei, Minrui 2010. An integrated method for the construction of compact fuzzy neural models. Lecture Notes in Computer Science 6215 , pp. 102-109. 10.1007/978-3-642-14922-1_14

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Uncontrolled Keywords:	Compact fuzzy neural models; Rule consequents and premises; Structure selection and parameter learning; Local model inputs
Additional Information:	Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings
Publisher:	Springer
ISSN:	0302-9743
Last Modified:	27 Oct 2022 09:10
URI:	https://orca.cardiff.ac.uk/id/eprint/64613

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