English  |  正體中文  |  简体中文  |  Items with full text/Total items : 56738/90513 (63%)
Visitors : 12090978      Online Users : 45
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    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

    Files in This Item:

    File SizeFormat
    1023-2796_15(1)p31-40.pdf415KbAdobe PDF234View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback