Non-fragile finite-time l2−l∞ state estimation for discrete-time Markov jump neural networks with unreliable communication links

Feng Li, Hao Shen, Mengshen Chen and Qingkai Kong

Applied Mathematics and Computation, 2015, vol. 271, issue C, 467-481

Abstract: This paper is concerned with the problem of finite-time l2−l∞ non-fragile state estimation for discrete-time Markov jump neural networks with unreliable communication links. The simultaneous occurrences of packet dropouts, time delays and the sensor nonlinearity stemmed from the unreliable communication links are considered. The focus is on the design of non-fragile state estimator such that the augmented estimation error system is mean-square stochastically finite-time stable with a prescribed level of l2−l∞ performance. By employing Lyapunov–Krasovskii approach and finite-time analysis theory, some sufficient conditions have been obtained for the existence of an admissible state estimator. Finally, a numerical example is employed to demonstrate the effectiveness of our proposed approach.

Keywords: Markov jump neural networks; Finite-time l2−l∞ state estimation; Non-fragile estimator; Unreliable communication links (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:271:y:2015:i:c:p:467-481

DOI: 10.1016/j.amc.2015.09.029

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