Finite-time synchronization control for uncertain Markov jump neural networks with input constraints View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-09

AUTHORS

Hao Shen, Ju H. Park, Zheng-Guang Wu

ABSTRACT

This paper is concerned with the problem of finite-time synchronization control for uncertain Markov jump neural networks in the presence of constraints on the control input amplitude. The parameter uncertainties under consideration are assumed to belong to a fixed convex polytope. By using a parameter-dependent Lyapunov functional and a simple matrix decoupling method, a sufficient condition is proposed to ensure that the considered networks are stochastically synchronized over a finite-time interval. The desired mode-independent controller parameters can be computed via solving a convex optimization problem. Finally, two chaos neural networks are employed to demonstrate the effectiveness of our proposed approach. More... »

PAGES

1709-1720

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11071-014-1412-3

DOI

http://dx.doi.org/10.1007/s11071-014-1412-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006452208


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