Exponential Dissipativity Analysis and State Estimation for Neural Networks with Mixed Interval Time-Varying Delays

被引：0
|
作者：
Yang, Li [1 ]
Li, Lei [1 ]
Ge, Dekui [1 ]
机构：
[1] Liaoning Univ, Sch Math, Shenyang 110036, Peoples R China
来源：
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年
关键词：
Exponential dissipative; Neural networks; State estimation; Linear matrix inequality; STABILITY; PASSIVITY;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the problem of delay-dependent exponential dissipative and state estimation is investigated for neural networks with mixed interval time-varying delays. By constructing appropriate Lyapunov-Krasovskii functional and employing Newton-Leibniz formulation and linear matrix inequality (LMI) technique, a new condition is developed to estimate the neuron states through observed output measurements such that the error-state system is exponential dissipative. A numerical example is provided to demonstrate the feasibility and effectiveness of our results.
引用
收藏
页码：266 / 271
页数：6
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