Stability Analysis for High-Order Dynamic Neural Networks with Time Delays

Author

Shen, Weimin ; Gu, Jason ; Shen, Yanjun

Author_Institution

Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS

fYear

2004

fDate

22-26 Aug. 2004

Firstpage

966

Lastpage

971

Abstract

This paper studies the problem of asymptotic stability for a class of high-order dynamic neural networks with time delays. Several useful lemmas are proved in this paper first. After that, the sufficient conditions for the asymptotic stability of the system with constant time delays are introduced, and also this asymptotic stability problem for the system with time-varying delays is put forward. In order to obtain above results, the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed to investigate this kind of problems. Finally, some numerical examples are given to illustrate the advantages of our approach

Keywords

Lyapunov methods; asymptotic stability; delay systems; delays; differential equations; functional equations; linear matrix inequalities; neurocontrollers; time-varying systems; Lyapunov-Krasovskii stability theory; asymptotic stability; functional differential equation; high-order dynamic neural network; linear matrix inequality; stability analysis; time delay; time-varying delay; Asymptotic stability; Control systems; Delay effects; Hopfield neural networks; Linear matrix inequalities; Neural networks; Nonlinear control systems; Sliding mode control; Stability analysis; Sufficient conditions; High-order Dynamic Neural Networks; Linear Matrix Inequality; Stability Analysis; Time delay;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on

Conference_Location

Shenyang

Print_ISBN

0-7803-8614-8

Type

conf

DOI

10.1109/ROBIO.2004.1521916

Filename

1521916