Asymptotic Stability Criterion for Fuzzy Recurrent Neural Networks with Interval Time-Varying Delay

被引：0
|
作者：
Chandran, R. [1 ]
Balasubramaniam, P. [2 ]
机构：
[1] Govt Arts Coll, Dept Comp Sci, Melur 626106, Tamil Nadu, India
[2] Deemed Univ, Gandhigram Rural Inst, Dept Math, Gandhigram, Tamil Nadu, India
来源：
MATHEMATICAL MODELLING AND SCIENTIFIC COMPUTATION | 2012年 / 283卷
关键词：
Fuzzy recurrent neural networks; Interval time-varying delay; Delay decomposition approach; Linear Matrix Inequalities; Maximum Admissible Upper Bound (MAUB); DEPENDENT STABILITY; DECOMPOSITION APPROACH; SYSTEMS;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper focuses on the delay-dependent, asymptotic stability for fuzzy recurrent neural networks (FRNNs) with interval time-varying delay. The delay interval is decomposed into multiple uniform subintervals. Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals. By employing these LKFs, new delay-dependent, asymptotic stability criterion is proposed in terms of Linear Matrix Inequalities (LMIs), which can be easily solved by MATLAB LMI toolbox. Numerical example is given to illustrate the effectiveness of the proposed method.
引用
收藏
页码：271 / +
页数：3
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