Stability Of Several Classes Of Neural Networks With Delays

In this paper, we study some important properties of the dynamical behaviors of solutions for several classes of neural network models with time delays, which includes the existence, uniqueness and exponential stability of the periodic solution and the equilibrium point.The paper is organized as follows:In the first chapter, the background and the history of neural networks are briefly addressed. Some known neural network models are introduced, the motivations and outline of this work are given. And some notations and lemmas are also listed in this chapter.In the second chapter, we consider the existence of the equilibrium point of a class of cellular neural network models with time-varying delays, we obtain some sufficient conditions for the global asymptotic stability of the equilibrium point.In the third chapter, we study the dynamical behavior of bidirectional associative memory (BAM)neural networks with distributed delays. Based on the continuation theorem of the coincidence degree theory and analytical technique, we obtain some sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the periodic solution.In the fourth chapter, we study Cohen-Grossberg neural networks with continuously distributed delays. Based on Halanay inequality, the continuation theorem of the coincidence degree theory, Cramer rule and analytical technique, we obtain some sufficient conditions ensuring the existence, uniqueness, and global exponential stability of periodic solution. We have renewed results and complement previously known results.