带马尔可夫跳和可变迟滞的非线性耦合神经网络同步问题

Synchronization of Nonlinearly-Coupled Neural Networks with Markov Jump and Time-Varying Delay

本文讨论了一类带马尔可夫跳和可变迟滞的非线性耦合神经网络的同步问题,其中模型的耦合强度是一个随机变量,网络的耦合结构根据一个连续时间马氏链来进行动态切换,并考虑了非线性的耦合项和时变时滞的影响.通过构造适当的Lyapunov函数,运用线性矩阵不等式方法,获得该类网络模型达到全局均方渐近同步的充分条件.最后,通过一个数值仿真的例子,论证了理论结果的有效性.

In this paper,the synchronization problem of a class of nonlinear coupled neural networks with Markovian jump and variable delay is discussed.The coupling strength of the model is a random variable,the coupling structure of the network switches dynamically according to a continuous time Markov chain,and the influence of nonlinear coupling term and time-varying delay is considered.By constructing a suitable Lyapunov function and using the linear matrix inequality method,the sufficient conditions for the global mean square asymptotic synchronization of the network model are obtained.Finally,a numerical example is given to demonstrate the effectiveness of the theoretical results.