Exponential Stabilization of Memristive Neural Networks With Time Delays

被引：275
|
作者：
Wu, Ailong [1 ,2 ]
Zeng, Zhigang [1 ,2 ]
机构：
[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
[2] Minist China, Key Lab Image Proc & Intelligent Control Educ, Wuhan 430074, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2012年 / 23卷 / 12期
关键词：
Hybrid systems; memristive neural networks; optimal control; stabilization; ROBUST STATE ESTIMATION; VARYING DELAYS; DISCRETE; SYNCHRONIZATION; SYSTEMS;
D O I：
10.1109/TNNLS.2012.2219554
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a general class of memristive neural networks with time delays is formulated and studied. Some sufficient conditions in terms of linear matrix inequalities are obtained, in order to achieve exponential stabilization. The result can be applied to the closed-loop control of memristive systems. In particular, several succinct criteria are given to ascertain the exponential stabilization of memristive cellular neural networks. In addition, a simplified and effective algorithm is considered for design of the optimal controller. These conditions are the improvement and extension of the existing results in the literature. Two numerical examples are given to illustrate the theoretical results via computer simulations.
引用
收藏
页码：1919 / 1929
页数：11
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