文章基本信息

标题：Periodicity and exponential stability of discrete-time neural networks with variable coefficients and delays
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作者：Hui Xu ; Ranchao Wu
期刊名称：Advances in Difference Equations
印刷版ISSN：1687-1839
电子版ISSN：1687-1847
出版年度：2013
卷号：2013
期号：1
页码：226
DOI：10.1186/1687-1847-2013-226
语种：English
出版社：Hindawi Publishing Corporation
摘要：Discrete analogues of continuous-time neural models are of great importance in numerical simulations and practical implementations. In the current paper, a discrete model of continuous-time neural networks with variable coefficients and multiple delays is investigated. By Lyapunov functional, continuation theorem of topological degree, inequality technique and matrix analysis, sufficient conditions guaranteeing the existence and globally exponential convergence of periodic solutions are obtained, without assuming the boundedness and differentiability of activation functions. To show the effectiveness of our method, an illustrative example is presented along with numerical simulations. MSC:34D23, 34K20, 39A12, 92B20.
关键词：coincidence degree ; discrete neural networks ; variable coefficient ; exponential stability ; periodic solution ; M -matrix