基于时滞分割法的具有leakage项时滞的离散型神经网络状态估计

摘要：研究一类具有leakage时滞的离散型神经网络的状态估计问题．通过构造新的Lyapunov泛函得到保证估计误差全局渐近稳定的充分条件，并通过求解一个线性矩阵不等式（LMI）得到状态估计器的增益矩阵．采用一种新的时滞分割方法将变时滞区间分割为多个子区间，使该结果在获得更小的保守性同时也降低了计算的复杂度．

A Delay Decomposition Approach to State Estimation for Discrete-Time Neural Networks with Time Delay in the Leakage Term

In this paper, the problem of state estimation for a class of discrete-time neural networks with leakage delay is investigated. A novel Lyapunov-Krasvskii functional is employed to derive a sufficient condition guaranteeing the estimation error to be globally asymptotically stable and the design of the gain matrix of the state estimator can be achieved by solving a linear matrix inequality （LMI）. The result is less conservative and, meanwhile, the computational complexity is reduced because a novel delay decomposition approach is developed which divides the variation interval of time-varying delay into several subintervals.