Existence and global exponential stability of anti-periodic solution of higher-order recurrent neural networks with distributed delays and impulse on time scales

Author

Ren, Yaping

Author_Institution

Dept. of Stat. & Math., Yunnan Univ. of Finances & Econ., Kunming, China

fYear

2012

fDate

21-23 April 2012

Firstpage

326

Lastpage

330

Abstract

By using the continuation theorem of coincidence degree theory, M-matrix theory and constructing some suitable Lyapunov functions, some sufficient conditions are obtained for the existence and global exponential stability of anti-periodic solutions of higher-order recurrent type neural networks with distributed delays and impulses on time scales without assuming the boundedness of the activation functions f_j, g_j.

Keywords

Lyapunov methods; asymptotic stability; delays; distributed control; matrix algebra; recurrent neural nets; transfer functions; Lyapunov functions; M-matrix theory; activation functions; antiperiodic solution; coincidence degree theory; continuation theorem; distributed delays; distributed impulses; global exponential stability; higher-order recurrent neural networks; time scales; Biological neural networks; Delay; Equations; Kernel; Lyapunov methods; Neurons; Functional difference equations; positive periodic solutions; strict-set-contraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on

Conference_Location

Yichang

Print_ISBN

978-1-4577-1414-6

Type

conf

DOI

10.1109/CECNet.2012.6201594

Filename

6201594