Abstract
This paper investigates the adaptive synchronization of memristor-based neural networks (MNNs) with discontinuous activations. First, by using a decoupling strategy, the extensively used models of MNNs are improved, which can describe the real dynamics more accurately. Moreover, we extend the asymptotic synchronization criteria for MNNs with the consideration of discontinuous activation functions. Additionally, a general adaptive controller is devised to synchronize the drive and response systems, and the sufficient stability conditions are established via Lyapunov functional method within the framework of differential inclusions. Finally, numerical simulations are conducted to show the effectiveness of the developed methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.