Global exponential stability and periodic solutions of Cohen-Grossberg neural networks with continuously distributed delays

Convergence dynamics of Cohen-Grossberg neural networks (CGNNs) with continuously distributed delays are discussed. Without assuming the differentiability and monotonicity of activation functions, the differentiability of amplification functions and the symmetry of synaptic interconnection weights, by skilfully constructing suitable Lyapunov functionals and employing inequality technique, three sets of easily verifiable delay independent criteria to guarantee the global exponential stability of a unique equilibrium point are given, and moreover, by constructing Poincaré mapping, other three sets of easily verifiable delay independent criteria to assure the existence and globally exponential stability of periodic solutions are obtained. Six examples are given to illustrate the theoretical results.