Finite-Time Fault-Tolerant State Estimation for Markovian Jumping Neural Networks With Two Delay Components

This paper focuses on the finite time fault tolerant state estimation of Markovian jumping neural networks with two delay components. Firstly, the mathematical expression of the state estimator is defined when the system components have faults and the system output has external disturbances. Then, an augmented Lyapunov-Krasovslii functional including additive delay information, state information and activation function information is used to derive stochastic finite time stability conditions for error state systems. In addition, some advanced reciprocally convex inequalities are used to obtain linear matrix inequality (LMI) conditions that are easy to solve. Finally, numerical simulation is carried out to verify the effectiveness of the proposed method. Numerical results show that the proposed state estimator can still guarantee the estimation performance in the finite time stability framework even if there are component faults.