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A hybrid approach to locally optimized interpretable partitions of fuzzy neural models

Lookup NU author(s): Dr Wanqing ZhaoORCiD


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Many learning methods have been proposed for Takagi-Sugeno-Kang fuzzy neural modelling. However, despite achieving good global performance, the local models obtained often exhibit eccentric behaviour which is hard to interpret. The problem here is to find a set of input space partitions and, hence, to identify the corresponding local models which can be easily understood in terms of system behaviour. A new hybrid approach for the construction of a locally optimized, functional-link-based fuzzy neural model is proposed in this paper. Unlike the usual linear polynomial models used for the rule consequent, the functional link artificial neural network (FLANN) is employed here to achieve a nonlinear mapping from the original model input space. Our hybrid learning method employs a modified differential evolution method to give the best fuzzy partitions along with the weighted fast recursive algorithm for the identification of each local FLANN. Results from a motorcycle crash dataset are included to illustrate the interpretability of the resultant model structure and the efficiency of the new learning technique.

Publication metadata

Author(s): Zhao W, Li K, Irwin GW, Niu Q

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Fuzzy Computation Theory and Applications (FCTA 2011)

Year of Conference: 2011

Pages: 461-465

Online publication date: 24/10/2011

Publisher: SciTePress


DOI: 10.5220/0003626304610465

Library holdings: Search Newcastle University Library for this item

ISBN: 9789898425836