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A Novel Adaptive Kernel for the RBF Neural Networks

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Abstract

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.

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Authors and Affiliations

  1. Faculty of Engineering Science and Technology, Iqra University, Defence View, Shaheed-e-Millat Road (Ext.), Karachi, 75500, Pakistan

    Shujaat Khan

  2. College of Engineering, Karachi Institute of Economics and Technology, Korangi Creek, Karachi, 75190, Pakistan

    Imran Naseem

  3. School of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia

    Imran Naseem & Roberto Togneri

  4. School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia

    Mohammed Bennamoun

Authors
  1. Shujaat Khan
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  2. Imran Naseem
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  3. Roberto Togneri
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  4. Mohammed Bennamoun
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Correspondence to Imran Naseem.

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Khan, S., Naseem, I., Togneri, R. et al. A Novel Adaptive Kernel for the RBF Neural Networks. Circuits Syst Signal Process 36, 1639–1653 (2017). https://doi.org/10.1007/s00034-016-0375-7

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  • DOI: https://doi.org/10.1007/s00034-016-0375-7

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