Global exponential stability for BAM neural networks with time-varying delays

Author

Zong, Guangdeng ; Wu, Yanfeng ; Hou, Linlin

Author_Institution

Sch. of Autom., Nanjing Univ. of Sci.&Technol., Nanjing

fYear

2008

fDate

2-4 July 2008

Firstpage

2500

Lastpage

2505

Abstract

The exponential stability problem for a class of BAM neural networks with time-varying delays is considered. Based on the Lyapunov function method, several sufficient conditions are provided ensuring the delayed BAM neural networks to have a unique equilibrium point, which is globally exponentially stable. All the results are given in terms of LMIs, which can be easily solved by resorting to Matlab tool-box. Simulations validate the correctness of the presented algorithm.

Keywords

Lyapunov methods; asymptotic stability; delays; linear matrix inequalities; neurocontrollers; time-varying systems; BAM neural networks; LMI; Lyapunov function method; Matlab tool-box; global exponential stability; time-varying delays; unique equilibrium point; Asymptotic stability; Automation; Computer networks; Delay effects; Electronic mail; Lyapunov method; Magnesium compounds; Neural networks; Neurons; Sufficient conditions; BAM neural networks; Lyapunov function; exponential stability; time-varying delay systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2008. CCDC 2008. Chinese

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-1733-9

Electronic_ISBN

978-1-4244-1734-6

Type

conf

DOI

10.1109/CCDC.2008.4597775

Filename

4597775