Discrete-Time Analogs for a Class of Continuous-Time Recurrent Neural Networks

Author

Liu, Pingzhou ; Han, Qing-Long

Author_Institution

Central Queensland Univ., Rockhampton

Volume

18

Issue

5

fYear

2007

Firstpage

1343

Lastpage

1355

Abstract

This paper is concerned with the problem of local and global asymptotic stability for a class of discrete-time recurrent neural networks, which provide discrete-time analogs to their continuous-time counterparts, i.e., continuous-time recurrent neural networks with distributed delay. Some stability criteria, which include some existing results as their special cases, are derived. A discussion about the dynamical consistence of discrete-time neural networks versus their continuous-time counterparts is provided. An unconventional finite difference method is proposed and an example is also given to show the effectiveness of the method.

Keywords

asymptotic stability; delays; discrete time systems; finite difference methods; recurrent neural nets; continuous-time recurrent neural network; discrete-time analogs; discrete-time recurrent neural network; distributed delay; finite difference method; global asymptotic stability; local asymptotic stability; stability criteria; Asymptotic stability; Australia; Differential equations; Filters; Informatics; Neural networks; Neurons; Propagation delay; Recurrent neural networks; Stability criteria; Delays; discrete-time analogs; recurrent neural networks; stability; Algorithms; Computer Simulation; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.891593

Filename

4298120