Multistability and multiperiodicity of delayed Cohen-Grossberg neural networks with a general class of activation functions

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Original languageEnglish
Pages (from-to)1734-1749
Journal / PublicationPhysica D: Nonlinear Phenomena
Issue number13
Publication statusPublished - 1 Aug 2008


In this paper, by using analysis approach and decomposition of state space, the multistability and multiperiodicity issues are discussed for Cohen-Grossberg neural networks (CGNNs) with time-varying delays and a general class of activation functions, where the general class of activation functions consist of nondecreasing functions with saturation's including piecewise linear functions with two corner points and standard activation functions as its special case. Based on the Cauchy convergence principle, some sufficient conditions are obtained for checking the existence and uniqueness of equilibrium points of the n-neuron CGNNs. It is shown that the n-neuron CGNNs can have 2n locally exponentially stable equilibrium points located in saturation regions. Also, some conditions are derived for ascertaining equilibrium points to be locally exponentially stable or globally exponentially attractive and to be located in any designated region. As an extension of multistability, some similar results are presented for ascertaining multiple periodic orbits when external inputs of the n-neuron CGNNs are periodic. Finally, three examples are given to illustrate the effectiveness of the obtained results. © 2008 Elsevier B.V. All rights reserved.

Research Area(s)

  • Cohen-Grossberg neural networks, Exponential stability, Exponentially attractive, Multiperiodicity, Multistability, Saturation regions