Attractor and Boundedness of Switched Stochastic Cohen‐Grossberg Neural Networks
Chuangxia Huang, Jie Ming Cao, Peng Wang · Discrete Dynamics in Nature and Society · 2016
We address the problem of stochastic attractor and boundedness of a class of switched Cohen‐Grossberg neural networks (CGNN) with discrete and infinitely distributed delays. With the help of stochastic analysis technology, the Lyapunov‐Krasovskii functional method, linear matrix inequalities technique (LMI), and the average dwell time approach (ADT), some novel sufficient conditions regarding the issues of mean‐square uniformly ultimate boundedness, the existence of a stochastic attractor, and the mean‐square exponential stability for the switched Cohen‐Grossberg neural networks are established. Finally, illustrative examples and their simulations are provided to illustrate the effectiveness of the proposed results.