In this paper, stochastic global exponential stability criteria for delayed im- pulsive Markovian jumping reaction-diffusion Cohen-Grossberg neural networks (CCNNs for short) are obtained by using a novel Lyapunov-K...In this paper, stochastic global exponential stability criteria for delayed im- pulsive Markovian jumping reaction-diffusion Cohen-Grossberg neural networks (CCNNs for short) are obtained by using a novel Lyapunov-Krasovskii functional approach, lin- ear matrix inequalities (LMIs for short) technique, Ito formula, Poincare inequality and Hardy-Poincare inequality, where the CGNNs involve uncertain parameters, partially un known Markovian transition rates, and even nonlinear p-Laplace diffusion (p 〉 1). It is worth mentioning that ellipsoid domains in Rm (m 〉 3) can be considered in numerical simulations for the first time owing to the synthetic applications of Poincare inequality and Hardy-Poincare inequality. Moreover, the simulation numerical results show that even the corollaries of the obtained results are more feasible and effective than the main results of some recent related literatures in view of significant improvement in the Mlowable upper bounds of delays.展开更多
基金supported by the National Natural Science Foundation of People Republic of China (61963033)the Key Project of Natural Science Foundation of Xinjiang (2021D01D10)。
基金supported by the National Basic Research Program of China(No.2010CB732501)the Scientific Research Fund of Science Technology Department of Sichuan Province(Nos.2010JY0057,2012JYZ010)+1 种基金the Sichuan Educational Committee Science Foundation(Nos.08ZB002,12ZB349)the Scientific Research Fund of Sichuan Provincial Education Department(Nos.14ZA0274,08ZB002,12ZB349)
文摘In this paper, stochastic global exponential stability criteria for delayed im- pulsive Markovian jumping reaction-diffusion Cohen-Grossberg neural networks (CCNNs for short) are obtained by using a novel Lyapunov-Krasovskii functional approach, lin- ear matrix inequalities (LMIs for short) technique, Ito formula, Poincare inequality and Hardy-Poincare inequality, where the CGNNs involve uncertain parameters, partially un known Markovian transition rates, and even nonlinear p-Laplace diffusion (p 〉 1). It is worth mentioning that ellipsoid domains in Rm (m 〉 3) can be considered in numerical simulations for the first time owing to the synthetic applications of Poincare inequality and Hardy-Poincare inequality. Moreover, the simulation numerical results show that even the corollaries of the obtained results are more feasible and effective than the main results of some recent related literatures in view of significant improvement in the Mlowable upper bounds of delays.