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State estimation for jumping recurrent neural networks with discrete and distributed delays

journal contribution
posted on 06.12.2017, 00:00 by Z Wang, Yurong Liu, X Liu
This paper is concerned with the state estimation problem for a class of Markovian neural networks with discrete and distributed time-delays. The neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov chain. The main purpose is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally asymptotically stable in the mean square. An effective linear matrix inequality approach is developed to solve the neuron state estimation problem. Both the existence conditions and the explicit characterization of the desired estimator are derived. Furthermore, it is shown that the traditional stability analysis issue for delayed neural networks with Markovian jumping parameters can be included as a special case of our main results. Finally, numerical examples are given to illustrate the applicability of the proposed design method.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

22

Issue

1

Start Page

41

End Page

48

Number of Pages

8

ISSN

0893-6080

Location

United Kingdom

Publisher

Elsevier Ltd

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Brunel University; Institute for Resource Industries and Sustainability (IRIS); Yangzhou da xue;

Era Eligible

Yes

Journal

Neural networks.