Global asymptotic stability analysis for neutral stochastic neural networks with time-varying delays |
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| Authors: | Weiwei Su Yiming Chen |
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| Institution: | 1. Department of Mathematics and Finance, Yunyang Teachers’ College, Hubei, Shiyan 442000, PR China;2. College of Information Science and Technology, Donghua University, Shanghai 201620, PR China;1. Department of Mathematics and Finance, Yunyang Teachers’ College, Hubei 442000, China;2. School of Finance, Nanjing Audit University, Jiangsu 211815, China;3. College of Science, Nanjing Audit University, Jiangsu 211815, China;4. School of Economics, South-Central University for Nationalities, Wuhan 430074, China;1. Department of Mathematics, Harbin Institute of Technology (Weihai), Weihai 264209, PR China;2. College of Science, China University of Petroleum (East China), Qingdao 266555, PR China;1. Taiyuan University of Technology, Taiyuan, Shanxi 030024, China;2. North University of China, Taiyuan, Shanxi 030051, China;3. Xi’an Jiaotong University, Xi’an, Shannxi 710049, China |
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| Abstract: | In this paper, the global asymptotic stability is investigated for a class of neutral stochastic neural networks with time-varying delays and norm-bounded uncertainties. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criteria are derived to ensure the global, robust, asymptotic stability of the addressed system in the mean square for all admissible parameter uncertainties. The criteria can be checked easily by the LMI Control Toolbox in Matlab. A numerical example is given to illustrate the feasibility and effectiveness of the results. |
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