一类具比例时滞细胞神经网络反周期解的指数稳定性

Exponential Stability of Anti-periodic for a Class of Cellular Neural Networks with Proportional Delays

本文针对一类具比例时滞细胞神经网络反周期解的全局指数稳定性进行研究.首先利用非线性变换将一类具比例时滞的细胞神经网络等价变换成一类具常时滞变系数的细胞神经网络.然后通过构造合适的时滞微分不等式和利用不等式技巧,得到了保证该系统反周期解的存在性和全局指数稳定性时滞依赖的充分条件.最后数值算例结果验证所得结论的正确性和与以往文献相比较低的保守性.

In this paper, the global exponential stability of anti-periodic solutions of a class of cellular neural networks with proportional delays is discussed. Firstly, a class of cellular neural networks with proportional delays is transformed equivalently into a class of cellular neural networks with constant delays and variable coefficients by a nonlinear transformation. Then, by establishing appropriate delay differential inequalities and applying inequality technique, a delay-dependent sufficient condition is obtained to ensure the existence and global exponential stability of anti-periodic solutions of the system. Finally, the numerical results indicate the proposed method is correct and less conservative than the existing results.