2011
DOI: 10.1007/s11071-011-0156-6
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New global exponential stability result to a general Cohen–Grossberg neural networks with multiple delays

Abstract: In this paper, we first discuss the existence of an equilibrium point to a general Cohen-Grossberg neural networks with multiple delays by means of using degree theory and linear matrix inequality (LMI) technique. Then by applying the existence result of an equilibrium point, linear matrix inequality technique and constructing a Lyapunov functional, we study the global exponential stability of equilibrium solution to the Cohen-Grossberg neural networks. Compared with known results, our results of global expone… Show more

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Cited by 20 publications
(2 citation statements)
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“…Cohen–Grossberg neural networks, which was first proposed by Cohen and Grossberg in 1983 . Since then, this new kind of neural networks have generated unprecedented worldwide interest due to its potential applications in classification, associative memory, parallel computation as well as solving optimization problems . Such applications heavily depend on the dynamic behavior of the networks, therefore, the analysis of these dynamic behaviors are a prerequisite step for the practical design of neural networks.…”
Section: Introductionmentioning
confidence: 99%
“…Cohen–Grossberg neural networks, which was first proposed by Cohen and Grossberg in 1983 . Since then, this new kind of neural networks have generated unprecedented worldwide interest due to its potential applications in classification, associative memory, parallel computation as well as solving optimization problems . Such applications heavily depend on the dynamic behavior of the networks, therefore, the analysis of these dynamic behaviors are a prerequisite step for the practical design of neural networks.…”
Section: Introductionmentioning
confidence: 99%
“…However, time delays as a source of instability and poor performance often appear in many neural networks such as Hopfield neural networks, cellular neural networks, Cohen-Grossberg neural networks and bidirectional associative memory neural networks. Therefore, the stability analysis for delayed neural networks has received substantial attention in recent years (see [15][16][17][18][19][20][21] and the references therein). In general, studying the dynamical behavior of delayed neural networks can be classified into two types: delay-independent stability and delay-dependent stability.…”
Section: Introductionmentioning
confidence: 99%