Stabilization and Synchronization of Neural Networks via Impulsive Adaptive Control. Academic Article

abstract

• This article addresses the stabilization and synchronization problems of coupled neural networks (NNs) via an impulsive adaptive control (IAC) strategy. Unlike the traditional fixed-gain-based impulsive methods, a novel discrete-time-based adaptive updating law for the impulsive gain is designed to maintain the stabilization and synchronization performance of the coupled NNs, where the adaptive generator only intermittently updates its data at the impulsive instants. Several stabilization and synchronization criteria for the coupled NNs are established based on the impulsive adaptive feedback protocols. Additionally, the corresponding convergence analysis are also provided. Finally, the effectiveness of the obtained theoretical results is illustrated using two comparison simulation examples.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Tan, X., He, W., Cao, J., & Huang, T.

complete list of authors

• Tan, Xuegang||He, Wangli||Cao, Jinde||Huang, Tingwen

publication date

• June 2023

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2162-237XISSN  Journal

keywords

• Impulsive Adaptive Control (iac)
• Neural Networks (nns)
• Stabilization
• Synchronization

PubMed Central ID

• 37379188

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2023.3287997

start page

• 1

end page

• 11

volume

• PP

issue

• 99

URL

• http://dx.doi.org/10.1109/tnnls.2023.3287997