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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.
The state estimation of discrete-time recursive neural systems with time-delay is studied.The state of neurons is estimated by network output.In the weak activation function hypothesis, a new Lyapunov functional is introduced to introduce a free-weight matrix and Jensen Inequality suf fi cient to ensure that the global stability of the system of errors is guaranteed. The conditions depend on the upper and lower bounds of the time-varying delay and are given in the form of linear matrix inequalities. Finally numerical examples show the effectiveness of the proposed method .