具有广义时滞的神经网络滞后同步分析

Lag Synchronization Analysis of Neural Networks with Generalized Delay

研究了具有广义时滞的神经网络滞后同步问题.首先,提出了一类包含离散时滞和比例时滞的一阶神经网络模型.其次,通过对一阶神经网络设计反馈控制策略,利用Lyapunov泛函理论和Barbalat引理,给出了驱动-响应神经网络的滞后同步条件.最后,通过3个数值实例来验证理论结果的有效性.

The lag synchronization problem of a class of neural networks with generalized delays is studied.Firstly,a class of first-order neural network models including discrete delays and proportional delays are proposed.Secondly,by designing a feedback control strategy for the first-order neural system,using the Lyapunov functional theory and the Barbarat lemma,the lag synchronization condition of the drive-response neural network is given.Finally,three numerical examples are given to verify the validity of the theoretical results.