Title :
Global exponential stability of generalized neural networks with time-varying delays
Author :
Wang, Gang ; Zhang, Huaguang ; Liu, Derong
Author_Institution :
Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang
Abstract :
In this paper, we essentially drop the requirement of Lipschitz condition on the activation functions. Only using physical parameters of neural networks, we propose some new criteria concerning global exponential stability of generalized neural networks with time-varying delays. Since these new criteria do not require the activation functions to be differentiate, bounded or monotone nondecreasing and the connection weight matrices to be symmetric, they are mild and more general than previously known criteria
Keywords :
asymptotic stability; circuit stability; delay circuits; neural nets; Lipschitz condition; activation functions; connection weight matrices; generalized neural networks; global exponential stability; time-varying delays; Artificial neural networks; Delay effects; Information science; Lyapunov method; Neural networks; Neurons; Stability analysis; Stability criteria; Sufficient conditions; Symmetric matrices;
Conference_Titel :
Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on
Conference_Location :
Island of Kos
Print_ISBN :
0-7803-9389-9
DOI :
10.1109/ISCAS.2006.1692692
