A Fractional Gradient Descent-Based RBF Neural Network

Shujaat Khan, Imran Naseem, Muhammad Ammar Malik, Roberto Togneri, Mohammed Bennamoun

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)5311-5332
Number of pages22
JournalCircuits, Systems and Signal Processing
Issue number12
Early online date19 May 2018
Publication statusPublished - 1 Dec 2018


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