Finite-Time Extended Dissipative Fault Estimate for Discrete-Time Markov Jumping Neural Networks Based on an Event-Triggered Approach

Abstract

This paper solves the finite-time extended dissipative fault estimate problem for discrete-time Markov jump neural networks based on an event-triggered approach in fully/partially known transition probability cases. Firstly, the systems are expanded into new systems treating sensor faults as states. Based on the proposed event-triggered scheme and an intermediate variable, an event-triggered intermediate observer is designed to estimate states, faults of actuator and sensor, and the intermediate variable, simultaneously. Next, the finite-time stability of error systems with extended dissipativity is analyzed, and the observer gains are shown in fully/partially known transition probability case, respectively, whose existence conditions are given. Finally, an example is given to illustrate the feasibility of the proposed scheme.