Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Exponential stability of continuous-time and discrete-time cellular neural networks with delays
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DOI：
10.1016/s0096-3003(01)00299-5
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发表时间：
2003-02-15
影响因子：
4
通讯作者：
Gopalsamy, K
中科院分区：
文献类型：
--
作者：
Mohamad, S;Gopalsamy, K
研究了具有离散时滞的连续时间细胞神经网络的收敛特性。通过使用李雅普诺夫泛函，我们获得了网络指数收敛于与常值输入源相关的平衡点的与延迟无关的充分条件。利用Halanay型不等式得到了网络全局指数稳定的充分条件。结果表明，从Halanay型不等式得到的估计改进从李雅普诺夫方法得到的估计。连续时间细胞神经网络的离散时间模拟制定和研究。结果表明，连续时间系统的收敛特性被保持的离散时间类似物没有任何限制施加在统一的离散化步长。(C)2002年爱思唯尔科学公司All rights reserved.
Convergence characteristics of continuous-time cellular neural networks with discrete delays are studied. By using Lyapunov functionals, we obtain delay independent sufficient conditions for the networks to converge exponentially toward the equilibria associated with the constant input sources. Halanay-type inequalities are employed to obtain sufficient conditions for the networks to be globally exponentially stable. It is shown that the estimates obtained from the Halanay-type inequalities improve the estimates obtained from the Lyapunov methods. Discrete-time analogues of the continuous-time cellular neural networks are formulated and studied. It is shown that the convergence characteristics of the continuous-time systems are preserved by the discrete-time analogues without any restriction imposed on the uniform discretization step size. (C) 2002 Elsevier Science Inc. All rights reserved.