Global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays

Author

Chen, Boshan ; Wang, Jun

Author_Institution

Dept. of Math., Hubei Normal Univ., China

Volume

16

Issue

6

fYear

2005

Firstpage

1440

Lastpage

1448

Abstract

In this paper, we present the analytical results on the global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays. Sufficient conditions are derived for ascertaining the existence, uniqueness and global exponential periodicity of the oscillatory solution of such recurrent neural networks by using the comparison principle and mixed monotone operator method. The periodicity results extend or improve existing stability results for the class of recurrent neural networks with and without time delays.

Keywords

asymptotic stability; delays; numerical stability; recurrent neural nets; sequential estimation; simulation; time-varying systems; comparison principle; global exponential periodicity; mixed monotone operator; oscillating connection; oscillating parameter; periodic oscillation; recurrent neural network; time-varying delay; Associative memory; Biological system modeling; Cellular neural networks; Delay effects; Differential equations; Neural networks; Neurons; Recurrent neural networks; Stability analysis; Sufficient conditions; Global exponential periodicity; global exponential stability; mixed monotone operator; neural networks; oscillating connections; periodic oscillation; time-varying delay; Animals; Biological Clocks; Computer Simulation; Humans; Models, Neurological; Nerve Net; Neural Networks (Computer); Oscillometry; Periodicity; Signal Processing, Computer-Assisted; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2005.857953

Filename

1528522