A systematic method for analyzing robust stability of interval neural networks with time-delays based on stability criteria.
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.
College of Mathematics and Econometrics, Hunan University, Changsha 410082, China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. Electronic address: zguo@hnu.edu.cn.
Zhenyuan
Z
Guo
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. Electronic address: jwang@mae.cuhk.edu.hk.
Jun
J
Wang
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. Electronic address: zyan@mae.cuhk.edu.hk.
Zheng
Z
Yan
D016571:Neural Networks, Computer; D013997:Time Factors; D035501:Uncertainty
Interval neural network; LMI; Robust stability; Time delay