Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/121201


Title: Convergent dynamics for multistable delayed neural networks
Authors: Shih, Chih-Wen
曾睿彬
Tseng, Jui-Pin
Contributors: 應數系
Date: 2008-05
Issue Date: 2018-12-04 15:42:48 (UTC+8)
Abstract: This investigation aims at developing a methodology to establish convergence of dynamics for delayed neural network systems with multiple stable equilibria. The present approach is general and can be applied to several network models. We take the Hopfield-type neural networks with both instantaneous and delayed feedbacks to illustrate the idea. We shall construct the complete dynamical scenario which comprises exactly 2n stable equilibria and exactly (3n − 2n) unstable equilibria for the n-neuron network. In addition, it is shown that every solution of the system converges to one of the equilibria as time tends to infinity. The approach is based on employing the geometrical structure of the network system. Positively invariant sets and componentwise dynamical properties are derived under the geometrical configuration. An iteration scheme is subsequently designed to confirm the convergence of dynamics for the system. Two examples with numerical simulations are arranged to illustrate the present theory.
Relation: Nonlinearity, Vol.21, pp.2361-2389
Data Type: article
DOI 連結: http://dx.doi.org/10.1088/0951-7715/21/10/009
Appears in Collections:[應用數學系] 期刊論文

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