Sensitivity analysis applied to the construction of radial basis function networks

Daming Shi, D.Y. Yeung, Junbin Gao

    Research output: Contribution to journalArticlepeer-review

    64 Citations (Scopus)


    Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. This paper proposes a novel learning algorithm for the construction of radial basis function using sensitivity analysis. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results show that our proposed sensitivity-based RBF classifier outperforms the conventional RBFs and is as accurate as support vector machine (SVM). Hence, sensitivity analysis is expected to be a new alternative way to the construction of RBF networks.
    Original languageEnglish
    Pages (from-to)951-957
    Number of pages7
    JournalNeural Networks
    Issue number7
    Publication statusPublished - 2005


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