Types Of Neural Network Model With Variable Time Delays Kinetic Study

This paper is composed of four chapters.In Chapter 1, we introduce the historical background of problems which will be investigated and the main work of this paper.In Chapter 2, by using M-matrix theory, some analysis techniques and constructing suitable Lyapunov functionals, we investigate the convergence of solutions and the existence and uniqueness of periodic solution for system:where ft and g, are the propagational signal functions defined onR. Several sufficient conditions guaranteeing the neural exponential stability as well as the existence and stability of periodic solution of the neural network are obtained. The global asymptotically stability of the corresponding neural network models with constant delays and coefficients is studied too. Our results improve and generalize some known results.In Chapter 3, the periodic oscillatory solutions and the global stability are studied for a class of continuous bi-directional associative memory neural network models with variable delays, and some simple and new sufficient conditions are given ensuring global exponential stability and the existence of periodic solutions of the neural nework. These results have important leading significance in the design and applications of global exponential stable BAM networks and periodic oscillatory BAM networks.