Improved structure optimization for fuzzy-neural networks

Pizzileo, Barbara, Li, Kang, Irwin, George W. and Zhao, Wanqing ORCID: https://orcid.org/0000-0001-6160-9547 2012. Improved structure optimization for fuzzy-neural networks. IEEE Transactions on Fuzzy Systems 20 (6) , pp. 1076-1089. 10.1109/TFUZZ.2012.2193587

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Abstract

Fuzzy-neural-network-based inference systems are well-known universal approximators which can produce linguistically interpretable results. Unfortunately, their dimensionality can be extremely high due to an excessive number of inputs and rules, which raises the need for overall structure optimization. In the literature, various input selection methods are available, but they are applied separately from rule selection, often without considering the fuzzy structure. This paper proposes an integrated framework to optimize the number of inputs and the number of rules simultaneously. First, a method is developed to select the most significant rules, along with a refinement stage to remove unnecessary correlations. An improved information criterion is then proposed to find an appropriate number of inputs and rules to include in the model, leading to a balanced tradeoff between interpretability and accuracy. Simulation results confirm the efficacy of the proposed method.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Uncontrolled Keywords:	Akaike’s information criteria; curse of dimensionality; fuzzy-neural networks (FNNs); input selection; rule selection
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1063-6706
Last Modified:	27 Oct 2022 09:10
URI:	https://orca.cardiff.ac.uk/id/eprint/64621

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