Existence And Exponential Stability Of Periodic Solutions For Two Discrete-Time Neural Networks

In this paper, a discrete-time bidirectional associative memory neural networks and a discrete-time Cohen-Grossberg neural networks are considered. In the first place, Some sufficient conditions ensuring the existence and global exponential stability of periodic solutions for the discrete-time bidirectional associative memory neural networks are derived by employing the theory of coincidence degree and inequality technique. And an example is worked out. In the next place, by employing Lyapunov function and the fixed point theory, we have derived several easily verifiable sufficient conditions ensuring the existence and global exponential stability of periodic solution for the discrete-time Cohen-Grossberg neural networks. An example is also worked out to demonstrate the advantages of our results.