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Discrete-time analogs for a class of continuous-time recurrent neural networks
1School of Computing Sciences, Faculty of Business and Informatics, Central Queensland University, Rockhampton, Qld 4702, Australia.
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.
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