Published December 2021 | Version v1
Journal article

Fixed-time synchronization for complex-valued BAM neural networks with time-varying delays via pinning control and adaptive pinning control

  • 1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590 (China)
  • 2. Institute of Complexity Science, Qingdao University, Qingdao 266071 (China)
  • 3. College of Automation Engineering, Qingdao University of Technology, Qingdao 266555 (China)

Description

The paper mainly studies the fixed-time synchronization of complex-valued BAM neural networks with time-varying delays via pinning control and adaptive pinning control. Firstly, the pinning control mechanism is designed to merely control partial nodes but not all nodes, which not only saves resources, but also improves the communication efficiency. Then, based on the appropriate Lyapunov function and some basic inequality techniques, a new fixed-time synchronization criterion through pinning control method is derived. Secondly, in order to reduce the computational burden, an adaptive pinning controller is designed by combining pinning and adaptive control, which improves the control performance and automatically adjust the control parameters, and new sufficient condition of the fixed-time synchronization under adaptive pinning control is obtained. Meanwhile, the upper bounds of the settling time are computed. Finally, two numerical examples show the effectiveness and feasibility of our results.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.chaos.2021.111583

Additional details

Identifiers

DOI
10.1016/j.chaos.2021.111583;
PII
S0960077921009371;

Publishing Information

Journal Title
Chaos, Solitons and Fractals
Journal Volume
153
Journal Page Range
vp.
ISSN
0960-0779

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
53098726
Subject category
S97: MATHEMATICAL METHODS AND COMPUTING;
Descriptors DEI
COMMUNICATIONS; CONTROL SYSTEMS; DESIGN; EFFICIENCY; LYAPUNOV METHOD; NEURAL NETWORKS; PERFORMANCE; SYNCHRONIZATION
Descriptors DEC
CALCULATION METHODS

Optional Information

Copyright
Copyright (c) 2021 Elsevier Ltd. All rights reserved.