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Improved structure optimization for fuzzy-neural networks

Lookup NU author(s): Dr Wanqing ZhaoORCiD

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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. © 2012 IEEE.

Publication metadata

Author(s): Pizzileo B, Li K, Irwin GW, Zhao W

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Fuzzy Systems

Year: 2012

Volume: 20

Issue: 6

Pages: 1076-1089

Print publication date: 01/12/2012

Online publication date: 03/05/2012

ISSN (print): 1063-6706

ISSN (electronic): 1941-0034

Publisher: IEEE

URL: https://doi.org/10.1109/TFUZZ.2012.2193587

DOI: 10.1109/TFUZZ.2012.2193587

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