Neural networks : the official journal of the International Neural Network Society
-
In this paper, we first investigate the existence of a periodic solution to interval general bidirectional associative memory (BAM) neural networks with multiple delays on time scales by the continuation theorem of coincidence degree theory. Then, by constructing a Lyapunov functional, we discuss the global exponential stability of the periodic solution for such neural networks on time scales. The paper unifies periodic discrete-time and continuous-time BAM neural networks under the same framework.
-
In this paper, we first discuss the existence and uniqueness of the equilibrium point of interval general BAM neural networks with reaction-diffusion terms and multiple time-varying delays by means of using degree theory. Then by applying the existence result of an equilibrium point and constructing a Lyapunov functional, we discuss global exponential stability for above neural networks. In the last section, we also give an example to demonstrate the validity of our global exponential stability result for above neural network.