Scale-Limited Lagrange Stability and Finite-Time Synchronization for Memristive Recurrent Neural Networks on Time Scales

被引：79
|
作者：
Xiao, Qiang [1 ,2 ]
Zeng, Zhigang [1 ,2 ]
机构：
[1] Educ Minist China, Guangdong HUST Ind Technol Res Inst, Guangdong Prov Key Lab Digital Mfg Equipment, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2017年 / 47卷 / 10期
关键词：
Memristive recurrent neural network (MRNN); scale-limited Lagrange stability; synchronization; time scale; VARYING DELAYS; DISTRIBUTED DELAYS; EXPONENTIAL STABILITY; PINNING CONTROL; STABILIZATION; DISCRETE; CRITERIA; SYSTEMS; DESIGN; SETS;
D O I：
10.1109/TCYB.2017.2676978
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The existed results of Lagrange stability and finite-time synchronization for memristive recurrent neural networks (MRNNs) are scale-free on time evolvement, and some restrictions appear naturally. In this paper, two novel scale-limited comparison principles are established by means of inequality techniques and induction principle on time scales. Then the results concerning Lagrange stability and global finite-time synchronization of MRNNs on time scales are obtained. Scaled-limited Lagrange stability criteria are derived, in detail, via nonsmooth analysis and theory of time scales. Moreover, novel criteria for achieving the global finite-time synchronization are acquired. In addition, the derived method can also be used to study global finite-time stabilization. The proposed results extend or improve the existed ones in the literatures. Two numerical examples are chosen to show the effectiveness of the obtained results.
引用
收藏
页码：2984 / 2994
页数：11
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