Open AccessFinite-Time Robust Stabilization for Stochastic Neural Networks
Author(s) -
Weixiong Jin,
Xiaoyang Liu,
Xiangjun Zhao,
Nan Jiang,
Zhengxin Wang
Publication year - 2012
Publication title -
abstract and applied analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.228
H-Index - 56
eISSN - 1687-0409
pISSN - 1085-3375
DOI - 10.1155/2012/231349
Subject(s) - nonlinear system , artificial neural network , mathematics , stability (learning theory) , control theory (sociology) , noise (video) , finite set , linear matrix inequality , class (philosophy) , computer science , mathematical optimization , control (management) , artificial intelligence , machine learning , mathematical analysis , physics , quantum mechanics , image (mathematics)
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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