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

• Published in: Neural networks Vol. 54; pp. 112 - 122
• Main Authors: Guo, Zhenyuan, Wang, Jun, Yan, Zheng
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.06.2014; Elsevier
• Subjects: Adaptative systems; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Control system analysis; Control theory. Systems; Exact sciences and technology; Interval neural network; LMI; Neural Networks (Computer); Robust stability; Time delay; Time Factors; Uncertainty; LMI; Robust stability; Time delay; Interval neural network; Compact space; Delay system; Stability criterion; Neural network; Exponential stability; Robust control; Asymptotic stability; Uncertain system; Global stabilization; Linear matrix inequality; Robustness; Delay time; Invarying system
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2014.03.002

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.