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Stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters |
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| Authors: | Linshan Wang Zhe Zhang |
| Affiliation: | a Department of Mathematics, Ocean University of China, Qingdao 266071, PR China b Department of Mathematics, Liaocheng University, Liaocheng 252059, PR China c College of Communication Engineering, Ocean University of China, Qingdao 266071, PR China |
| Abstract: | Some criteria for the global stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters are presented. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish some easy-to-test criteria of global exponential stability in the mean square for the stochastic neural networks. The criteria are computationally efficient, since they are in the forms of some linear matrix inequalities. |
| Keywords: | Stochastic reaction-diffusion neural networks Time-varying delay Jumping parameters Linear matrix inequality Stochastic exponential stability in the mean square |
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