Letters: Convergence of gradient method with penalty for Ridge Polynomial neural network

Authors:
Xin Yu;Qingfeng Chen
Affiliations:
School of Computer, Electronics and Information, Guangxi University, Nanning 53004, China;School of Computer, Electronics and Information, Guangxi University, Nanning 53004, China
Venue:
Neurocomputing
Year:
2012

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Abstract

In this paper, a penalty term is added to the conventional error function to improve the generalization of the Ridge Polynomial neural network. In order to choose appropriate learning parameters, we propose a monotonicity theorem and two convergence theorems including a weak convergence and a strong convergence for the synchronous gradient method with penalty for the neural network. The experimental results of the function approximation problem illustrate the above theoretical results are valid.