Fixed-time synchronization for complex-valued BAM neural networks with time-varying delays via pinning control and adaptive pinning control
Creators
- 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.2021.111583Additional 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.