Global asymptotic stability for a class of neural networks with time-varying delays

Author

Yingxin Guo ; Chao Xu

Author_Institution

State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China

fYear

2014

fDate

8-10 Aug. 2014

Firstpage

72

Lastpage

76

Abstract

The paper deals with the globally asymptotically stability of dynamical neural networks with time-varying delays. The sufficient conditions for the globally asymptotically stable of the neural networks are obtained by Lyapunov-Razumikhin technique. Particularly, we discuss the stability conditions which do not require the activation functions to be differential, bounded, or monotone nondecreasing. Two examples are also applied to illustrate the efficiency of the results.

Keywords

Lyapunov methods; asymptotic stability; delay systems; neurocontrollers; time-varying systems; transfer functions; Lyapunov-Razumikhin technique; activation function; global asymptotic stability; neural network; stability condition; sufficient condition; time-varying delay; Associative memory; Asymptotic stability; Biological neural networks; Delays; Neurons; Stability analysis; Global asymptotic stability; Lyapunov functionals; Lyapunov-Razumikhin technique; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese

Conference_Location

Yantai

Print_ISBN

978-1-4799-4700-3

Type

conf

DOI

10.1109/CGNCC.2014.7007221

Filename

7007221