期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2014
卷号：9
期号：9
页码：151-158
DOI：10.14257/ijmue.2014.9.9.17
出版社：SERSC
摘要：This paper investigates a gradient descent algorithm with penalty for a recurrent neural network. The penalty we considered here is a term proportional to the norm of the weights. Its primary roles in the methods are to control the magnitude of the weights. After proving that all of the weights are automatically bounded during the iteration process, we also present some deterministic convergence results for this learning methods, indicating that the gradient of the error function goes to zero(weak convergence) and the weight sequence goes to a fixed point(strong convergence), respectively. A numerical example is provided to support the theoretical analysis.
