文章基本信息

标题：Local stability conditions for discrete-time cascade locally recurrent neural networks
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作者：Krzysztof Patan
期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2010
卷号：20
期号：1
DOI：10.2478/v10006-010-0002-x
出版社：De Gruyter Open
摘要：The paper deals with a specific kind of discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the network considered is a locally recurrent globally feedforward. A crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates local stability conditions for the analysed class of neural networks using Lyapunov's first method. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem, a gradient projection method is adopted. The efficiency and usefulness of the proposed approach are justified by using a number of experiments
关键词：locally recurrent neural network; stability; stabilization; learning; constrained optimization