Title
Asymptotic stability and synchronization of fractional delayed memristive neural networks with algebraic constraints
Abstract
The asymptotic stability and synchronization of fractional delayed memristive neural networks with algebraic constraints in Riemann-Liouville sense will be investigated in this article. First, algebraic constraints are introduced for the first time into the existing fractional delayed memristive neural networks, and a new fractional singular delayed memristive neural networks (FSDMNNs) model is presented. Then, within the framework of Filippov's solution, a less conservative result for the asymptotic stability of FSDMNNs is obtained by Lyapunov-Krasovskii functional. Subsequently, the appropriate feedback scheme and adaptive scheme are designed to synchronize FSDMNNs and two sufficient conditions are acquired. In addition, the results not only address the influence of delays and algebraic constraints, but can also easily detect and synchronize the actual memristive neural networks. Finally, numerical simulations frankly confirm the correctness and validity of the derived results. (C) 2022 Published by Elsevier B.V.
Year
DOI
Venue
2022
10.1016/j.cnsns.2022.106694
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
Keywords
DocType
Volume
Stability and synchronization, Fractional delayed, Memristive, Algebraic constraints
Journal
114
ISSN
Citations 
PageRank 
1007-5704
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Xiang Wu100.34
Shutang Liu25111.49
Huiyu Wang300.34