Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks

Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks

Blog Article

We address the problem of stochastic attractor and boundedness of a class of switched Cohen-Grossberg neural verona wig 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 rubbermaid 8 gallon trash can the switched Cohen-Grossberg neural networks are established.Finally, illustrative examples and their simulations are provided to illustrate the effectiveness of the proposed results.