Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/4918
Title: | Asymptotic stability for neural networks with mixed time-delays: The discrete-time case |
Authors: | Liu, Y Wang, Z Liu, X |
Keywords: | Discrete-time neural networks;Stochastic neural networks;Asymptotic stability;Discrete time-delays;Distributed time-delays;Lyapunov–Krasovskii functional;Linear matrix inequality |
Issue Date: | 2009 |
Publisher: | Elsevier |
Citation: | Neural Networks, 22(1): 67-74, Jan 2009 |
Abstract: | This paper is concerned with the stability analysis problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability analysis problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition. |
Description: | This is the post print version of the article. The official published version can be obtained from the link - Copyright 2009 Elsevier Ltd |
URI: | http://bura.brunel.ac.uk/handle/2438/4918 |
DOI: | http://dx.doi.org/10.1016/j.neunet.2008.10.001 |
ISSN: | 0893-6080 |
Appears in Collections: | Computer Science Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Fulltext.pdf | 189.15 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.