Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects Academic Article

abstract

• 2015, The Natural Computing Applications Forum. In this paper, a generalized memristor-based recurrent neural network model with variable delays and impulse effects is considered. By using an impulsive delayed differential inequality and Lyapunov function, the exponential stability of the impulsive delayed memristor-based recurrent neural networks is investigated. Several exponential and uniform stability criteria of this impulsive delayed system are derived, which promotes the study of memristor-based recurrent neural networks. Finally, the effectiveness of obtained results is illustrated by two numerical examples.

authors

• Huang, Tingwen

published proceedings

• NEURAL COMPUTING & APPLICATIONS

author list (cited authors)

• Wang, H., Duan, S., Li, C., Wang, L., & Huang, T.

citation count

• 14

complete list of authors

• Wang, Huamin||Duan, Shukai||Li, Chuandong||Wang, Lidan||Huang, Tingwen

publication date

• April 2017

publisher

• Springer Nature  Publisher

published in

• Neural Computing and Applications  Journal

keywords

• Exponential Stability
• Impulse Effects
• Impulsive Differential Inequality
• Memristor-based Recurrent Neural Networks

Digital Object Identifier (DOI)

• 10.1007/s00521-015-2094-6

start page

• 669

end page

• 678

volume

• 28

issue

• 4

URL

• http://dx.doi.org/10.1007/s00521-015-2094-6