Fault-Tolerant State Estimation for Markov Jump Neural Networks With Time-Varying Delays

This brief focuses on the fault-tolerant state estimation for neural networks with Markov jump parameters and time-varying delays. The objective is to estimate the system states regardless of whether sensor faults occur or not. First, an augmented estimation error system is constructed by taking a fault estimation vector into account. Then by selecting a suitable Lyapunov-Krasovskii functional (LKF) and using the integral inequality technique, sufficient conditions that guarantee the $H_{\infty }$ performance of state estimation errors are presented, and the solution of fault-tolerant state estimator are given in terms of linear matrix inequalities (LMIs). The proposed technique is illustrated by a numerical example.