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

Zhao, Wanqing ORCID: https://orcid.org/0000-0001-6160-9547, Li, Kang, Irwin, George W. and Niu, Qun 2011. A hybrid approach to locally optimized interpretable partitions of fuzzy neural models. Presented at: International Conference on Fuzzy Computation Theory and Applications (FCTA 2011), Paris, France, 24-26 October 2011. Published in: Rosa, Agostinho ed. ECTA 2011, FCTA 2011 proceedings of the International Conference on Evolutionary Computation Theory and Applications and International Conference on Fuzzy Computation Theory and Applications. SciTePress, p. 461. 10.5220/0003626304610465

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Engineering
Uncontrolled Keywords:	Fuzzy neural systems; Interpretable model; Differential evolution; Weighted fast recursive algorithm; ANFIS
Publisher:	SciTePress
ISBN:	9789898425836
Last Modified:	27 Oct 2022 09:10
URI:	https://orca.cardiff.ac.uk/id/eprint/64614

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