Title :
Stability conditions for uncertain BAM neural networks of neutral-type with time-varying delays
Author :
Tang, Yi ; Liu, Guoquan ; Wang, Runhua ; Liu, Yucheng
Author_Institution :
Sch. of Electron. & Inf. Eng., Chongqing Univ. of Sci. & Technol., Chongqing, China
Abstract :
This paper deals with the robust stability for uncertain bidirectional associative memory (BAM) neural networks of neutral-type with time-varying delays. The parameter uncertainties are assumed to be norm bounded, the discrete delays and neutral delays are time-varying delays. The combined method, which is based on the Lyapunov-Krasovskii functional (LKF) combined with some inequality techniques, is used to investigate this problem. Then, by constructing a new LKF, using the Newton-Leibniz formula and introducing some free weighting matrices, some sufficient conditions are proposed to guarantee global asymptotic robust stability for the considered systems. It is also shown that the obtained results can be established in terms of linear matrix inequality (LMI).
Keywords :
Lyapunov methods; asymptotic stability; linear matrix inequalities; neurocontrollers; robust control; time-varying systems; Lyapunov-Krasovskii functional; Newton-Leibniz formula; global asymptotic robust stability; linear matrix inequality; neutral-type; parameter uncertainty; stability condition; time-varying delays; uncertain BAM neural network; uncertain bidirectional associative memory neural network; Associative memory; Asymptotic stability; Biological neural networks; Delay; Stability criteria; BAM neural networks; Linear matrix inequality; Neutral-type; Robust stability; parameter uncertainties;
Conference_Titel :
Cognitive Informatics & Cognitive Computing (ICCI*CC ), 2011 10th IEEE International Conference on
Conference_Location :
Banff, AB
Print_ISBN :
978-1-4577-1695-9
DOI :
10.1109/COGINF.2011.6016135
