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Finite-time synchronization control for uncertain Markov jump neural networks with input constraints. (English) Zbl 1331.92019

Summary: 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.

MSC:

92B20 Neural networks for/in biological studies, artificial life and related topics
93C95 Application models in control theory
60J27 Continuous-time Markov processes on discrete state spaces
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