Global exponential periodicity of a class of neural networks with recent-history distributed delaysYang, 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 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1016/j.chaos.2004.11.014 Abstract/SummaryIn 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.
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