Finite-time stochastic stabilization for BAM neural networks with uncertainties |
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| Authors: | Xiaoyang Liu Nan Jiang Jinde Cao Shumei Wang Zhengxin Wang |
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| Institution: | 1. School of Computer Science & Technology, Jiangsu Normal University, Xuzhou 221116, Jiangsu, PR China;2. School of Business, Jiangsu Normal University, Xuzhou 221116, Jiangsu, PR China;3. Department of Mathematics, Southeast University, Nanjing 210096, Jiangsu, PR China;4. College of Science, Nanjing University of Posts and Telecommunications, Nanjing 210046, Jiangsu, PR China |
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| Abstract: | This paper is concerned with the finite-time stabilization for a class of stochastic BAM neural networks with parameter uncertainties. Compared with the previous references, a continuous stabilizator is designed for stabilizing the states of stochastic BAM neural networks in finite time. Based on the finite-time stability theorem of stochastic nonlinear systems, several sufficient conditions are proposed for guaranteeing the finite-time stability of the controlled neural networks in probability. Meanwhile, the gains of the finite-time controller could be designed by solving some linear matrix inequalities. Furthermore, for the stochastic BAM neural networks with uncertain parameters, the problem of robust finite-time stabilization could also be ensured as well. Finally, two numerical examples are given to illustrate the effectiveness of the obtained theoretical results. |
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