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Impulsive effects on stability of Cohen–Grossberg-type bidirectional associative memory neural networks with delays

✍ Scribed by Qinghua Zhou; Li Wan

Publisher
Elsevier Science
Year
2009
Tongue
English
Weight
630 KB
Volume
10
Category
Article
ISSN
1468-1218

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✦ Synopsis

In this paper, the exponential stability is investigated for a class of Cohen-Grossberg-type bidirectional associative memory neural networks with delays and impulsive effects. By using Lyapunov functionals, the analysis method, inequality technique and the properties of an M-matrix, the delay-independent sufficient conditions ensuring the existence, uniqueness and global exponential stability of the equilibrium point are derived. The obtained results are less restrictive than those given in the earlier literature, and the boundedness and differentiability of the activation functions are removed. An illustrative example is given to demonstrate the effectiveness of the obtained results.

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✍ Xiaohu Wang; Qingyi Guo; Daoyi Xu 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 204 KB

In this paper, we study the impulsive stochastic Cohen-Grossberg neural networks with mixed delays. By establishing an Loperator differential inequality with mixed delays and using the properties of M-cone and stochastic analysis technique, we obtain some sufficient conditions ensuring the exponenti