A graph-theoretic approach to boundedness of stochastic Cohen–Grossberg neural networks with Markovian switching

作者：

Highlights：

• White noise and color noise are considered in the novel stochastic neural networks.

• A new approach combining Lyapunov method and some graph theory is presented.

• The sufficient principle obtained by using M-matrix technique is easy to be verified.

• The impact of Markovian switching and coupled structure are showed by two examples.

摘要

Highlights•White noise and color noise are considered in the novel stochastic neural networks.•A new approach combining Lyapunov method and some graph theory is presented.•The sufficient principle obtained by using M-matrix technique is easy to be verified.•The impact of Markovian switching and coupled structure are showed by two examples.

论文关键词：Cohen–Grossberg neural networks,Boundedness,Graph theory,Markovian switching,M-matrix

论文评审过程：Available online 20 April 2013.

论文官网地址：https://doi.org/10.1016/j.amc.2013.03.048