Title :
Delay-dependent exponential stability of delayed neural networks with time-varying delay
Author :
He, Yong ; Wu, Min ; She, Jin-hua
Author_Institution :
Sch. of Inf. Sci. & Eng., Central South Univ. of Technol., Changsha, China
fDate :
7/1/2006 12:00:00 AM
Abstract :
In this brief, free-weighting matrices are employed to express the relationship between the terms in the Leibniz-Newton formula; and based on that relationship, a new delay-dependent exponential-stability criterion is derived for delayed neural networks with a time-varying delay. Two numerical examples demonstrate the improvement this method provides over existing ones.
Keywords :
asymptotic stability; delays; linear matrix inequalities; neural nets; time-varying systems; Leibniz-Newton formula; delay-dependent criterion; delayed neural networks; exponential stability; free-weighting matrix; linear matrix inequality; time-varying delay; Asymptotic stability; Convergence; Delay effects; Educational programs; Helium; Information science; Linear matrix inequalities; Neural networks; Neurons; Stability criteria; Delay-dependent criterion; exponential stability; free-weighting matrix approach; linear matrix inequality (LMI); neural networks;
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
DOI :
10.1109/TCSII.2006.876385
