@article {IOPORT.06116349,
    author       = {Jin, Weixiong and Liu, Xiaoyang and Zhao, Xiangjun and Jiang, Nan and Wang, Zhengxin},
    title        = {Finite-time robust stabilization for stochastic neural networks.},
    year         = {2012},
    journal      = {Abstract and Applied Analysis},
    volume       = {2012},
    issn         = {1085-3375},
    pages        = {Article ID 231349, 15 p.},
    publisher    = {Hindawi Publishing Corporation, New York, NY},
    doi          = {10.1155/2012/231349},
    abstract     = {Summary: We are 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.},
    identifier   = {06116349},
}