Title
Uniformly stable and attractive of fractional-order memristor-based neural networks with multiple delays.
Abstract
Memristive neural networks (MNN) have been wildly studied. Nevertheless, fractional -order memristive neural networks (FMNN) still remain a wide-open dilemma. This paper addresses the problem of FMNN systems with multiple delays and grew several related theories through the study. First, the existence of the system equilibrium point is investigated based on contraction mapping principle, and a new sufficient criterion is obtained. Second, the delay-free uniform stability of the system is discussed by employing differential inclusion theory. Third, a novel asymptotic stability criterion is proposed which is less conservative. Finally, one descriptive numerical example and simulation results emphasize the accuracy and reliability of the proposed results.
Year
DOI
Venue
2019
10.1016/j.amc.2018.11.028
Applied Mathematics and Computation
Keywords
Field
DocType
Fractional-order systems,Memristive neural networks,Multiple delays,Uniformly stable,Attractive
Differential inclusion,Applied mathematics,Mathematical optimization,Memristor,Contraction mapping,Equilibrium point,Exponential stability,Artificial neural network,Mathematics
Journal
Volume
ISSN
Citations 
347
0096-3003
0
PageRank 
References 
Authors
0.34
34
5
Name
Order
Citations
PageRank
Xueqi Yao111.37
Shouming Zhong21470121.41
Taotao Hu383.83
Hong Cheng470365.27
Dian Zhang5628.27