摘要
本文研究了一类随机时滞递归神经网络的指数稳定性问题.利用非负鞅收敛定理和Lyapunov泛函的方法,获得了这类神经网络矩指数稳定性的新的代数准则,所给代数准则简单易用.一个具体实例用来说明稳定性判别准则的应用.
The moment exponential stability for a stochastic delay recurrent neural networks is discussed by means of a nonnegative semi-martingale convergence theorem and Lyapunov functional method. The new algebraic criteria of the moment exponential stability for a stochastic delay recurrent neural network is derived, and these algebraic criteria are simple and practical. An example is also given for illustration.
出处
《数学杂志》
CSCD
北大核心
2014年第3期487-496,共10页
Journal of Mathematics
基金
Supported by National Natural Science Foundation of China(10971240)
National Natural Science Foundation of Huaihai Institute of Technology(KK06004)
关键词
随机递归神经网络
变时滞
矩指数稳定性
LYAPUNOV指数
stochastic recurrent neural network
time-varying delay
moment exponentialstability
Lyapunov exponent
