Novel results on asymptotic stability and synchronization of fractional-order memristive neural networks with time delays: The 0 < δ ≤ 1 case

This paper investigates the asymptotic stability and synchronization of fractional-order (FO) memristive neural networks with time delays. Based on the FO comparison principle and inverse Laplace transform method, the novel sufficient conditions for the asymptotic stability of a FO nonlinear system are given. Then, based on the above conclusions, the sufficient conditions for the asymptotic stability and synchronization of FO memristive neural networks with time delays are investigated. The results in this paper have a wider coverage of situations and are more practical than the previous related results. Finally, the validity of the results is checked by two examples.(c) 2023 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the asymptotic stability and synchronization of fractional-order memristive neural networks with time delays. Sufficient conditions for the asymptotic stability of a fractional-order nonlinear system are given based on the FO comparison principle and inverse Laplace transform method. Then, the sufficient conditions for the asymptotic stability and synchronization of fractional-order memristive neural networks with time delays are investigated. The results in this paper are more practical and have a wider coverage of situations compared to previous related results. The validity of the results is checked by two examples.