Journal
NEUROCOMPUTING
Volume 179, Issue -, Pages 238-245Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2015.11.089
Keywords
Non-fragile state estimation; Estimator gain variations; Markovian jumping; Time delays; Nonlinearity
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Funding
- National Natural Science Foundation of China [61422301, 61374127]
- Outstanding Youth Science Foundation of Heilongjiang Province [JC2015016]
- Technology Foundation for Selected Overseas Chinese Scholar from the Ministry of Personnel of China
- Alexander von Humboldt Foundation of Germany
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In this paper, the non-fragile state estimation problem is investigated for a class of discrete-time neural networks subject to Markovian jumping parameters and time delays. In terms of a Markov chain, the mode switching phenomenon at different times is considered in both the parameters and the discrete delays of the neural networks. To account for the possible gain variations occurring in the implementation, the gain of the estimator is assumed to be perturbed by multiplicative norm-bounded uncertainties. We aim to design a non-fragile state estimator such that, in the presence of all admissible gain variations, the estimation error converges to zero exponentially. By adopting the Lyapunov-Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimator that guarantees the stability of the overall estimation error dynamics. The explicit expression of such estimators is parameterized by solving a convex optimization problem via the semi-definite programming method. A numerical simulation example is provided to verify the usefulness of the proposed methods. (C) 2015 Elsevier B.V. All rights reserved.
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