Finite-time stabilization for positive Markovian jumping neural networks

Chengcheng Ren and Shuping He

Applied Mathematics and Computation, 2020, vol. 365, issue C

Abstract: This paper addresses finite-time boundedness and stabilization problem for n-neuron uncertain positive Markovian jumping neural networks (MJNNs). Firstly, we analyze the positive MJNNs in the input-free case and then propose a sufficient condition to ensure the input-free finite-time boundedness. Then applying the state feedback scheme, a suitable finite-time stabilizable controller is devised to guarantee the positiveness of the closed-loop MJNNs. Moreover, some sufficient conditions for the existence of the controller gain solutions are proposed and proved by using the stochastic Lyapunov-Krasovskii functional approach and linear matrix inequalities techniques. Finally, we give two simulation examples to demonstrate the effectiveness and feasibility of the proposed methods.

Keywords: Markovian jumping neural networks; Positiveness; Finite-time stabilizable; Stochastic Lyapunov-Krasovskii functional (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:365:y:2020:i:c:s009630031930623x

DOI: 10.1016/j.amc.2019.124631

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