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2012 Finite-Time Robust Stabilization for Stochastic Neural Networks
Weixiong Jin, Xiaoyang Liu, Xiangjun Zhao
Abstr. Appl. Anal. 2012(SI04): 1-15 (2012). DOI: 10.1155/2012/231349


This paper is concerned with the finite-time stabilization for a class of stochastic neural networks (SNNs) with noise perturbations. The purpose of the addressed problem is to design a nonlinear stabilizator which can stabilize the states of neural networks in finite time. Compared with the previous references, a continuous stabilizator is designed to realize such stabilization objective. Based on the recent finite-time stability theorem of stochastic nonlinear systems, sufficient conditions are established for ensuring the finite-time stability of the dynamics of SNNs in probability. Then, the gain parameters of the finite-time controller could be obtained by solving a linear matrix inequality and the robust finite-time stabilization could also be guaranteed for SNNs with uncertain parameters. Finally, two numerical examples are given to illustrate the effectiveness of the proposed design method.


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Weixiong Jin. Xiaoyang Liu. Xiangjun Zhao. "Finite-Time Robust Stabilization for Stochastic Neural Networks." Abstr. Appl. Anal. 2012 (SI04) 1 - 15, 2012.


Published: 2012
First available in Project Euclid: 5 April 2013

Digital Object Identifier: 10.1155/2012/231349

Rights: Copyright © 2012 Hindawi

Vol.2012 • No. SI04 • 2012
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