Paper Info
Title
A new predefined-time stability theorem and its application in the synchronization of memristive complex-valued BAM neural networks
Abstract
In this paper, two novel and general predefined-time stability lemmas are given and applied to the predefined-time synchronization problem of memristive complex-valued bidirectional associative memory neural networks (MCVBAMNNs). Firstly, different from the generally fixed-time stability lemma, the setting of an adjustable time parameter in the derived predefined-time stability lemma causes it to be more flexible and more general. Secondly, the model studied in the complex-valued BAM neural networks model, which is different from the previous discussion of the real part and imaginary part respectively. It is more practical to study the complex-valued nonseparation. Thirdly, two effective controllers are designed to realize the synchronization performance of BAM neural networks based on the predefined-time stability, and the analysis is given based on general predefined-time synchronization. Finally, the correctness of the theoretical derivation is verified by numerical simulation. A secure communication scheme based on predefined-time synchronization of MCVBAMNNs is proposed, and the effectiveness and superiority of the results are proved.
Year
DOI
Venue
2022
10.1016/j.neunet.2022.05.031
Neural Networks
Keywords
DocType
Volume
Complex-valued neural networks,Bidirectional associative memory neural networks,Memristor,Predefined-time stability
Journal
153
Issue
ISSN
Citations 
1
0893-6080
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Aidi Liu100.68
Hui Zhao2169.22
Qingjie Wang300.34
Sijie Niu44710.94
Xizhan Gao500.68
Chuan Chen6557.63
Lixiang Li753346.82