Finite-time stochastic stabilization for BAM neural networks with uncertainties
Finite-time stochastic stabilization for BAM neural networks with uncertainties
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DOI：
10.1016/j.jfranklin.2013.05.027
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Xiaoyang Liu;Nan Jiang;Jinde Cao;Shumei Wang;Zhengxin Wang
中科院分区：
文献类型：
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作者：
Xiaoyang Liu;Nan Jiang;Jinde Cao;Shumei Wang;Zhengxin Wang
研究了一类具有参数不确定性的随机BAM神经网络的有限时间镇定问题。与以往文献相比，本文设计了一种连续镇定器，用于在有限时间内镇定随机BAM神经网络的状态。基于随机非线性系统的有限时间稳定性定理，给出了保证受控神经网络概率有限时间稳定的几个充分条件。同时，有限时间控制器的增益可以通过求解线性矩阵不等式来设计。此外，对于具有不确定参数的随机BAM神经网络，也可以保证鲁棒有限时间镇定问题。最后，通过两个数值例子说明了所得理论结果的有效性.
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.