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Adaptive extended fuzzy basis function network

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Abstract

The structure of the extended fuzzy basis function network (EFBFN) is firstly proposed, and the least squares (LS) method is used to design it by fixing the widths of the hidden units in EFBFN. Then, to enhance the performance of the obtained EFBFN ulteriorly, a novel evolutionary algorithm based on LS and the hybrid of evolutionary programming and particle swarm optimization (LS-EPPSO) is proposed, in which we use EPPSO to tune the parameters of the premise part in EFBFN, and the LS algorithm to decide the consequent parameters in it simultaneously. The enhanced EFBFN whose parameters are refined automatically using LS-EPPSO is thus called adaptive EFBFN. In the simulation part, the proposed method to construct AEFBFN is employed to model a three input nonlinear function and to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed.

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Acknowledgments

This work was supported by the Outstanding Young Scholars Fund (No. 60225006) and Innovative Research Group Fund (No. 60421002) of the Natural Science Foundation of China.

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  1. College of Electrical Engineering, Zhejiang University, Hangzhou, China

    B. Ye, C. Z. Zhu & Y. J. Cao

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  1. B. Ye
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  2. C. Z. Zhu
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  3. Y. J. Cao
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Correspondence to B. Ye.

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Ye, B., Zhu, C.Z. & Cao, Y.J. Adaptive extended fuzzy basis function network. Neural Comput & Applic 16, 197–206 (2007). https://doi.org/10.1007/s00521-006-0051-0

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