Published May 2013 | Version v1
Journal article

Exponential synchronization of coupled memristive neural networks via pinning control

  • 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/10.1088/1674-1056/22/5/050504

Additional details

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