A Novel Adaptive Kernel for the RBF Neural Networks

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    43 Citations (Scopus)


    In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation.

    Original languageEnglish
    Pages (from-to)1639-1653
    Number of pages15
    JournalCircuits, Systems, and Signal Processing
    Issue number4
    Publication statusPublished - 1 Apr 2017


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