A systematic method for analyzing robust stability of interval neural networks with time-delays based on stability criteria

Publication Type:
Journal Article
Neural Networks, 2014, 54 pp. 112 - 122
Issue Date:
Filename Description Size
1-s2.0-S0893608014000562-main.pdfPublished Version609.49 kB
Adobe PDF
Full metadata record
This paper presents a systematic method for analyzing the robust stability of a class of interval neural networks with uncertain parameters and time delays. The neural networks are affected by uncertain parameters whose values are time-invariant and unknown, but bounded in given compact sets. Several new sufficient conditions for the global asymptotic/exponential robust stability of the interval delayed neural networks are derived. The results can be casted as linear matrix inequalities (LMIs), which are shown to be generalizations of some existing conditions. Compared with most existing results, the presented conditions are less conservative and easier to check. Two illustrative numerical examples are given to substantiate the effectiveness and applicability of the proposed robust stability analysis method. © 2014 Elsevier Ltd.
Please use this identifier to cite or link to this item: