Exponential stability and periodicity of cellular neural networks with time delay

This paper investigates the problem of exponential stability and periodicity for a class of delayed cellular neural networks (DCNN’s). By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions for exponential stability and periodicity are derived via constructing Lyapunov functional. Those conditions suitable are associated with some initial value and are represented by some blocks of the interconnection matrix. Two examples are discussed to illustrate the main results.