Title :
Discrete-Time Analogs for a Class of Continuous-Time Recurrent Neural Networks
Author :
Liu, Pingzhou ; Han, Qing-Long
Author_Institution :
Central Queensland Univ., Rockhampton
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891593
