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Asymptotic stability for neural networks with mixed time-delays: The discrete-time case
- Source :
-
Neural Networks . Jan2009, Vol. 22 Issue 1, p67-74. 8p. - Publication Year :
- 2009
-
Abstract
- Abstract: This paper is concerned with the stability analysis problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability analysis problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 08936080
- Volume :
- 22
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 36191728
- Full Text :
- https://doi.org/10.1016/j.neunet.2008.10.001