Improved Structure Optimization for Fuzzy-Neural Networks

Pizzileo, Barbara, Li, Kang, Irwin, George W. and Zhao, Wanqing (2012) Improved Structure Optimization for Fuzzy-Neural Networks. IEEE Transactions on Fuzzy Systems, 20 (6). pp. 1076-1089. ISSN 1063-6706

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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
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 03 Jul 2020 23:58
Last Modified: 06 Aug 2020 23:51
URI: https://ueaeprints.uea.ac.uk/id/eprint/75912
DOI: 10.1109/TFUZZ.2012.2193587

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