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A Fractional Gradient Descent-Based RBF Neural Network

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Abstract

In this research, we propose a novel fractional gradient descent-based learning algorithm (FGD) for the radial basis function neural networks (RBF-NN). The proposed FGD is the convex combination of the conventional, and the modified Riemann–Liouville derivative-based fractional gradient descent methods. The proposed FGD method is analyzed for an optimal solution in a system identification problem, and a closed form Wiener solution of a least square problem is obtained. Using the FGD, the weight update rule for the proposed fractional RBF-NN (FRBF-NN) is derived. The proposed FRBF-NN method is shown to outperform the conventional RBF-NN on four major problems of estimation namely nonlinear system identification, pattern classification, time series prediction and function approximation.

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Acknowledgements

We like to thank anonymous reviewers for their valuable suggestions.

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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. Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju, 61452, Republic of Korea

    Muhammad Ammar Malik

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

    Mohammed Bennamoun

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  1. Shujaat Khan
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Correspondence to Imran Naseem.

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Khan, S., Naseem, I., Malik, M.A. et al. A Fractional Gradient Descent-Based RBF Neural Network. Circuits Syst Signal Process 37, 5311–5332 (2018). https://doi.org/10.1007/s00034-018-0835-3

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