Title :
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
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;
Conference_Titel :
Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4799-4700-3
DOI :
10.1109/CGNCC.2014.7007221
