pth moment exponential synchronization analysis for a class of stochastic neural networks with mixed delays |
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| Affiliation: | 1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;2. College of Mathematics and Econometrics, Hunan University, Changsha 410082, China;3. Centre for Artificial Intelligence, University of Technology Sydney, Australia;1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;2. College of Information Science and Technology, Donghua University, Shanghai 201620, China;3. Freshman Education Department, Yangtze University, Jing Zhou, Hubei 434023, China;4. School of Mathematics and Statistics, Anyang Normal University, Anyang 455000, China;5. Department of Mathematics and Finance, Yunyang Teachers’ College, Shiyan, Hubei 442000, China;1. College of Information Engineering, Xiangtan University, Xiangtan 411105, China;2. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;3. College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China;1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China;2. Engineering Research Center of Internet of Things Technology Applications (Ministry of Education), Jiangnan University, Wuxi 214122, PR China |
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| Abstract: | In this paper, the issue of pth moment exponential synchronization of a class of stochastic neural networks with mixed delays is investigated. By establishing two new integro-differential inequalities, some new sufficient conditions ensuring pth moment exponential synchronization are obtained. Compared with previous method, our method does not resort to any Lyapunov function. The results obtained in this paper generalize some earlier works reported in the literature. Some strict constraints of time delays and kernel functions are removed. Two numerical examples are presented to illustrate the validity of the main results. |
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