State estimation of neural networks with time-varying delays and Markovian jumping parameter based on passivity theory View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-10

AUTHORS

S. Lakshmanan, Ju H. Park, D. H. Ji, H. Y. Jung, G. Nagamani

ABSTRACT

In this paper, the state estimation problem is investigated for neural networks with time-varying delays and Markovian jumping parameter based on passivity theory. 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 stable in the mean square and passive from the control input to the output error. Based on the new Lyapunov–Krasovskii functional and passivity theory, delay-dependent conditions are obtained in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate effectiveness of the proposed method and results. More... »

PAGES

1421-1434

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11071-012-0544-6

DOI

http://dx.doi.org/10.1007/s11071-012-0544-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018409670


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N-Triples is a line-based linked data format ideal for batch operations.

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Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

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