Improved Structure Optimization for Fuzzy-Neural Networks

Barbara Pizzileo, Kang Li, George W. Irwin, Wanqing Zhao

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1076-1089
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Volume20
Issue number6
DOIs
Publication statusPublished - 1 Dec 2012

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