Existence and exponential stability of periodic solution of delayed CohenGrossberg neural networks via impulsive control
This paper focuses on the existence, uniqueness and global exponential stability of periodic solution for CohenGrossberg neural networks (CGNN) with periodic coefficients and time-varying delays. Some novel delay-independent criteria are obtained by using contraction mapping theorem and comparison principle. The present results improve and extend those in many publications, and shows that under some delay-independent criteria, the CGNNs may admit a periodic solution, which is globally exponential stable via impulsive controller even if it is originally unstable or divergent. Two examples with numerical simulations are given to demonstrate the efficiency of theoretical results.
stability periodic solution cohengrossberg neural network impulse time-varying delay contraction mapping theorem comparison principle