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Global exponential periodicity of a class of neural networks with recent-history distributed delays

Yang, X. F., Liao, X. F., Megson, G. M. and Evans, D. J. (2005) Global exponential periodicity of a class of neural networks with recent-history distributed delays. Chaos Solitons & Fractals, 25 (2). pp. 441-447. ISSN 0960-0779

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To link to this item DOI: 10.1016/j.chaos.2004.11.014

Abstract/Summary

In this paper, we propose to study a class of neural networks with recent-history distributed delays. A sufficient condition is derived for the global exponential periodicity of the proposed neural networks, which has the advantage that it assumes neither the differentiability nor monotonicity of the activation function of each neuron nor the symmetry of the feedback matrix or delayed feedback matrix. Our criterion is shown to be valid by applying it to an illustrative system. (c) 2005 Elsevier Ltd. All rights reserved.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science
ID Code:15475
Uncontrolled Keywords:ASSOCIATIVE MEMORY NETWORKS, TIME-VARYING DELAYS, ASYMPTOTIC STABILITY, TRANSMISSION DELAYS, EXISTENCE, DYNAMICS, CNNS, ATTRACTIVITY, COEFFICIENTS, OSCILLATION

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