Stability and Stabilization of Time-Delayed Fractional Order Neural Networks via Matrix Measure

被引：1
|
作者：
Wang, Fei [1 ]
Yang, Yongqing [1 ]
Lu, Jianquan [2 ]
Cao, Jinde [2 ]
机构：
[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Peoples R China
[2] Southeast Univ, Sch Math, Nanjing 210096, Jiangsu, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS, PT I | 2017年 / 10261卷
关键词：
Fractional-order; Matrix measure; Neural networks; Stability; Delay; SYNCHRONIZATION; SYSTEMS;
D O I：
10.1007/978-3-319-59072-1_58
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The stability problem of delayed neural networks with fractional order dynamics has been studied in this paper. Several criteria for the stability of the equilibrium point are derived via matrix measure method and fractional order differential inequality. All criteria are formed as matrix measure, which can be easy to verify in practice. Based on which, feedback controllers are designed to stabilize a kind of chaotic fractional order neural network. Finally, two simulations are given to check the theoretical results and compare with some exist results.
引用
收藏
页码：493 / 501
页数：9
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