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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Sun, B; Wen, S; Wang, S; Huang, T; Chen, Y; Li, P

Published Date

  • March 7, 2020

Published In

Volume / Issue

  • 380 /

Start / End Page

  • 133 - 140

Electronic International Standard Serial Number (EISSN)

  • 1872-8286

International Standard Serial Number (ISSN)

  • 0925-2312

Digital Object Identifier (DOI)

  • 10.1016/j.neucom.2019.11.003

Citation Source

  • Scopus