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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/50550


    Title: Hybrid Recursive Particle Swarm Optimization Learning Algorithm in the Design of Radial Basis Function Networks
    Authors: Feng, Hsuan-ming;Chen, Ching-yi;余繁;Ye, Fun
    Contributors: 淡江大學電機工程學系
    Keywords: normalized fuzzy c-means;particle swarm optimization;recursive least-squares;radial basis function networks
    Date: 2007-03
    Issue Date: 2010-08-09 19:44:44 (UTC+8)
    Publisher: Springer
    Abstract: In this paper, an innovative hybrid recursive particle swarm optimization (HRPSO) learning algorithm with normalized fuzzy c-mean (NFCM) clustering, particle swarm optimization (PSO) and recursive least-squares (RLS) is proposed to generate radial basis function networks (RBFNs) modeling system with small numbers of descriptive radial basis functions (RBFs) for fast approximating two complex and nonlinear functions. Simulation results demonstrate that the generated NFCM-based learning schemes approach the desired modeling systems within the smaller population sizes.
    Relation: Journal of Marine Science and Technology 15(1), pp.31-40
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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