Quantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm

TitleQuantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm
Publication TypeJournal Article
Year of Publication2020
AuthorsB Sun, S Wen, S Wang, T Huang, Y Chen, and P Li
JournalNeurocomputing
Volume380
Start Page133
Pagination133 - 140
Date Published03/2020
Abstract

In this paper, we investigate quantized synchronization control problem of memristive neural networks (MNNs) with time-varying delays via super-twisting algorithm. A feedback controller is introduced with quantized method. To enormously reduce the computational complexity of the controller under super-twisting algorithm, two quantized control schemes are proposed with uniform quantizer and logarithmic quantization. We obtain some sufficient conditions of specific control plans to guarantee that the driving MNNs can synchronize with the response MNNs. A neoteric Lyapunov functional is designed to analyze the synchronization problem. Finally, in this paper ending, some illustrative examples are given in support of our results.

DOI10.1016/j.neucom.2019.11.003
Short TitleNeurocomputing