Fault-Tolerant State Estimation for Markov Jump Neural Networks With Time-Varying Delays
Wenjuan Lin, Guoqiang Tan, Qing‐Guo Wang, Jinpeng Yu
Abstract
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> 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.