Exponential Stability Analysis About There Classes Of Stochastic Cohen-Grossberg Neural Networks With Delays

In this paper, stability of stochastic Cohen-Grossberg neural net-works for three different classes of types was studied. In chapter two, exponential stability of stochastic Cohen-Grossberg neural network with distributed delays was discussed. A set of novel sufficient conditions on pth moment exponential stability were given for the considered system by using Razumikin-type theorem and Hardy inequality technology. A numerical example was given to illustrate the effectiveness of the results. In chapter three, exponential stability of stochastic Cohen-Grossberg neural network with mixed time-varying delays was researched. By using Lyapunov function, Razumikin-type theorem and inequality technique, some sufficient conditions were given to ensure the pth moment exponential stability of the system.An example was also given to illustrate that our results are correct and effectiveness. In chapter four, robust stability of stochastic Cohen-Grossberg neural networks with time-varying was considered. Based on the Lyapunov-Krasovksii function and linear matrix inequality technology, some sufficient conditions were given to ensure the global robust exponential stability in the mean square.