NEW ROBUST STABILITY of DISCRETE-TIME STOCHASTIC NEURAL NETWORKS with MARKOVIAN JUMPING PARAMETERS

被引：0
作者：
Shi, Gui-Ju [1 ]
He, Hai-Kuo [2 ]
Gao, Jun-Ling [3 ]
Wang, Zhan-Hui [4 ]
机构：
[1] Hebei Univ Sci & Technol, Coll Grad, Shijiazhuang 050018, Peoples R China
[2] Hebei Univ Sci & Technol, Coll Sci, Shijiazhuang 050018, Peoples R China
[3] Hebei Univ Sci & Technol, Coll Informat Sci & Engn, Shijiazhuang 050018, Peoples R China
[4] Hebei Univ Sci & Technol, Coll Environm Sci & Technol, Shijiazhuang 050018, Peoples R China
来源：
PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年
关键词：
Discrete-time; stochastic neural networks; Markovian Jumping Parameters; linear matrix inequality; EXPONENTIAL STABILITY; VARYING DELAYS; LMI APPROACH; SYSTEMS;
D O I：
10.1109/ICMLC.2009.5212108
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The asymptotic stability in the mean square is studied for a class of discrete-time uncertain stochastic neural networks with Markovian jumping parameters in this paper. By introducing some free weighting matrices and constructing a right Lyapunov-Krasovskii functional, we get an novel global asymptotic stability criteria. Conditions are proposed to guarantee the robust stability of discrete-time uncertain stochastic neural networks via linear matrix inequality approach. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the results.
引用
收藏
页码：2598 / +
页数：2
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