TY - JOUR
T1 - Observer design for switched recurrent neural networks
T2 - An average dwell time approach
AU - Lian, Jie
AU - Feng, Zhi
AU - Shi, Peng
PY - 2011/10
Y1 - 2011/10
N2 - This paper is concerned with the problem of observer design for switched recurrent neural networks with time-varying delay. The attention is focused on designing the full-order observers that guarantee the global exponential stability of the error dynamic system. Based on the average dwell time approach and the free-weighting matrix technique, delay-dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities. The error-state decay estimate is also given. Then, the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results. Finally, four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods.
AB - This paper is concerned with the problem of observer design for switched recurrent neural networks with time-varying delay. The attention is focused on designing the full-order observers that guarantee the global exponential stability of the error dynamic system. Based on the average dwell time approach and the free-weighting matrix technique, delay-dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities. The error-state decay estimate is also given. Then, the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results. Finally, four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods.
KW - Average dwell time method
KW - exponential stability
KW - observer design
KW - switched neural networks
KW - time-varying delay
UR - https://www.scopus.com/pages/publications/80053624322
U2 - 10.1109/TNN.2011.2162111
DO - 10.1109/TNN.2011.2162111
M3 - 文章
C2 - 21824843
AN - SCOPUS:80053624322
SN - 1045-9227
VL - 22
SP - 1547
EP - 1556
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 10
M1 - 5975222
ER -