The Stability Of Several Types Of Neural Network Analysis

The delayed neural networks exhibiting the rich and colorful dynamical behaviors are important part of the delayed neural systems. Due to their importance and potential application in signal processing, image processing, artificial intelligence, optimizing problems and so on, the dynamical issues of delayed neural networks have attracted worldwide attention in recent years. These theies mainly focus on the global stability for several delayed neural networks. More specifically, the main contributions are as follows:1) The analysis of stability for the cellular Neural Networks with DelayBy constructing novel Lyapunov functional, using the stability theoryof functional differential equations, and combining with the technique of inequality analysis, we obtained the sufficient criteria which with and without time-varying delays for the globally asymptotic stability of the neural network, independent of the delays. These conclusions have extended the results in the given references.2) Asymptotic Stability of Cohen-Grossberg Neural NetworksBased on the construction of a new Lyapunov functional, by applying the Young inequality method, and introducing many real parameters, a series of new and useful criteria for the globally asymptotic stability of Cohen-Grossberg Neural Networks are derived. The delays under some conditions are harmless.3) Delay-dependent Asymptotic Stability of a Two-neuron System with Different Time DelaysThrough constructing Lyapunov functional and applying the technique of inequality analysis, we obtain sufficient criteria to ensure global asymptotic stability of the equilibrium of the neural network. Our results have given some new criteria for delayed neural networks.