Published May 2013
| Version v1
Journal article
Exponential synchronization of coupled memristive neural networks via pinning control
Creators
- 1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)
Description
This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled subsystems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results. (general)
Availability note (English)
Available from http://dx.doi.org/10.1088/1674-1056/22/5/050504Additional details
Identifiers
Publishing Information
- Journal Title
- Chinese Physics. B
- Journal Volume
- 22
- Journal Issue
- 5
- Journal Page Range
- [10 p.]
- ISSN
- 1674-1056
INIS
- Country of Publication
- China
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 45031994
- Subject category
- S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
- Descriptors DEI
- ALGORITHMS; COMPUTERIZED SIMULATION; CONTROL; NETWORK ANALYSIS; NEURAL NETWORKS; NUMERICAL ANALYSIS; SYNCHRONIZATION; TOPOLOGY
- Descriptors DEC
- MATHEMATICAL LOGIC; MATHEMATICS; SIMULATION