Exponential Stability of Switched Time-varying Delayed Neural Networks with All Modes Being Unstable Academic Article

abstract

• 2015, Springer Science+Business Media New York. This paper aims to design an appropriate switching law to stabilize the switched neural networks with time-varying delays when all subsystems are unstable. By using the discretized Lyapunov function approach and the extended comparison principle for impulsive systems, the stability of switched delayed neural networks composed full of unstable subsystems is analyzed and a computable sufficient condition is derived in the framework of dwell time. The effectiveness of the proposed results is illustrated by a numerical example.

authors

• Huang, Tingwen

published proceedings

• NEURAL PROCESSING LETTERS

author list (cited authors)

• Qi, J., Li, C., Huang, T., & Zhang, W.

citation count

• 15

complete list of authors

• Qi, Jiangtao||Li, Chuandong||Huang, Tingwen||Zhang, Wei

publication date

• April 2016

publisher

• Springer Nature  Publisher

published in

• Neural Processing Letters  Journal

keywords

• Comparison Principle
• Discretized Lyapunov Function
• Dwell Time
• Exponential Stability
• Neural Network
• Time-varying Delay
• Unstable Subsystem

Digital Object Identifier (DOI)

• 10.1007/s11063-015-9428-3

start page

• 553

end page

• 565

volume

• 43

issue

• 2

URL

• http://dx.doi.org/10.1007/s11063-015-9428-3