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Lookup NU author(s): Dr Wanqing ZhaoORCiD
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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.
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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