Several Types Of Neural Network Model Of Kinetic Analysis

In this thesis, we investigate dynamic behaviors of several classes of neural networks. First, we derive new criteria for the existence and global attractivity of almost periodic sequence solution and k-almost periodic sequence solution of discrete-time cellular neural networks. These criteria based on system parameters are easy for us to check. Our results can also provide us with relevant estimates on how precise such networks can perform during real-time computations in applications. Furthermore, we develop a topological approach of wa(z|·)ewski-type which is suitable for differential equations with piecewise constant argument to investigate qualitative behavior of cellular neural networks with piecewise constant argument. Secondly, by using Laypunov-Krasovskii functional and inequality technique, some sufficient conditions for the existence and global stability of equilibrium are attained for bidirectional associative memory (BAM) neural networks with nonlinear impulses. The exponential stability of periodic solutions for BAM neural networks with finite distributed delays is also discussed. At last, we discuss dynamical properties of Cohen-Grossberg neural networks. By using Laypunov-Krasovskii functional and homeomorphism mapping, some new sufficient conditions are established for global exponential p-stability of a unique equilibrium for second order Cohen-Grossberg neural networks with transmission delays and an unsupervised Hebbian-type learning behavior. Moreover, the learning dynamic behavior of neurons is also given. For impulsive Cohen-Grossberg networks with distributed delays, the obtained results of stability of equilibrium are easy to verify, meanwhile we remove the boundedness of activation functions and invertibility of the suitable behaved functions. It is believed that these results are suitable and useful for the design and applications of general Cohen-Grossberg networks.