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Abstract and Applied Analysis
Volume 2012 (2012), Article ID 231349, 15 pages
Research Article

Finite-Time Robust Stabilization for Stochastic Neural Networks

1Department of Mathematics and Applied Mathematics, Lianyungang Teacher’s College, Lianyungang 222006, China
2School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, China
3School of Economics, Jiangsu Normal University, Xuzhou 221116, China
4College of Science, Nanjing University of Posts and Telecommunications, Nanjing 210046, China

Received 6 September 2012; Accepted 26 September 2012

Academic Editor: Ju H. Park

Copyright © 2012 Weixiong Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.