作　　者： ; ; ;

机构地区： 北京科技大学计算机与通信工程学院

出　　处： 《控制与决策》 2011年第3期423-427,共5页

摘　　要： 研究时滞离散递归神经系统的状态估计问题.通过网络输出对神经元的状态进行估计.在较弱的激活函数假设下,通过构造一个新的Lyapunov泛函,引入一个自由权矩阵,并结合Jensen不等式得到了确保误差系统全局指数稳定的充分条件.所得条件依赖于时变时滞的上界和下界,并以线性矩阵不等式的形式给出.最后的数值算例表明了所提出方法的有效性. State estimation for discrete-time recurrent neural networks with time-varying delay is investigated. A state estimator to estimate the neuron states is designed through available output measurements. Under a weaker assumption on the activation functions, by constructing a new Lyapunov functional, introducing a free weighting matrix and employing the Jensen inequality, a sufficient condition is established to ensure the globally exponential stability of the error system. The condition is dependent on both the lower bound and upper bound of the time-varying delay, and is given in terms of linear matrix inequality. Finally, a numerical example shows the effectiveness of the proposed method.

关 键 词： 时变时滞 指数稳定 状态估计器 线性矩阵不等式

领　　域： [自动化与计算机技术] [自动化与计算机技术]