Exponential Stability of Neutral-Type Impulsive Markovian Jump Neural Networks with General Incomplete Transition Rates

被引：6
|
作者：
Liu, Yunlong [1 ]
Zhang, Ce [2 ]
Kao, Yonggui [3 ]
Hou, Chongsheng [1 ]
机构：
[1] Weifang Univ, Coll Informat & Control Engn, Weifang 261061, Peoples R China
[2] Harbin Inst Technol Weihai, Sch Comp Sci & Technol, Weihai 264209, Peoples R China
[3] Harbin Inst Technol Weihai, Sch Sci, Weihai 264209, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2018年 / 47卷 / 02期
关键词：
Neutral-type Markovian jump neural networks; Stability; Impulsive; Generally uncertain transition rates; TIME-VARYING DELAYS; FUNCTIONAL-DIFFERENTIAL EQUATIONS; SAMPLED-DATA SYNCHRONIZATION; STOCHASTIC STABILITY; LINEAR-SYSTEMS; SINGULAR SYSTEMS; MIXED DELAYS; PARAMETERS; PROBABILITY; DESIGN;
D O I：
10.1007/s11063-017-9650-2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is devoted to the investigation of exponential stability of Neutral-type impulsive Markovian jump neural networks with mixed time-varying delays and generally uncertain transition rates (GUTRs). Each transition rate can be completely unknown or only its estimate value is known in this GUTR model. This new uncertain model is more general than the existing ones. By utilizing Lyapunov-Krasovkii functional approach and linear matrix inequality technology, some novel globally exponentially stable results are derived. An example is given to show the effectiveness of the obtained results.
引用
收藏
页码：325 / 345
页数：21
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