期刊
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
卷 37, 期 12, 页码 5311-5332出版社
SPRINGER BIRKHAUSER
DOI: 10.1007/s00034-018-0835-3
关键词
Artificial neural networks; Radial basis function; Nonlinear system identification; Time series prediction; Kernel function; Function approximation; Fractional-order calculus; modified Riemann-Liouville derivative; Wiener solution
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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