A parsimonious radial basis function-based neural network for data classification

Shing Chiang Tan, Chee Peng Lim, Junzo Watada

    研究成果: Conference contribution

    抄録

    The radial basis function neural network trained with a dynamic decay adjustment (known as RBFNDDA) algorithm exhibits a greedy insertion behavior as a result of recruiting many hidden nodes for encoding information during its training process. In this chapter, a new variant RBFNDDA is proposed to rectify such deficiency. Specifically, the hidden nodes of RBFNDDA are re-organized through the supervised Fuzzy ARTMAP (FAM) classifier, and the parameters of these nodes are adapted using the Harmonic Means (HM) algorithm. The performance of the proposed model is evaluated empirically using three benchmark data sets. The results indicate that the proposed model is able to produce a compact network structure and, at the same time, to provide high classification performances.

    本文言語English
    ホスト出版物のタイトルSmart Innovation, Systems and Technologies
    出版社Springer Science and Business Media Deutschland GmbH
    ページ49-60
    ページ数12
    42
    ISBN(印刷版)9783319212081
    DOI
    出版ステータスPublished - 2016
    イベント5th International Conference on Intelligent Decision Technologies, 2013 - Sesimbra, Portugal
    継続期間: 2013 6 262013 6 28

    出版物シリーズ

    名前Smart Innovation, Systems and Technologies
    42
    ISSN(印刷版)21903018
    ISSN(電子版)21903026

    Other

    Other5th International Conference on Intelligent Decision Technologies, 2013
    CountryPortugal
    CitySesimbra
    Period13/6/2613/6/28

    ASJC Scopus subject areas

    • Computer Science(all)
    • Decision Sciences(all)

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