| Title |
H∞ State Estimation of Static Delayed Neural Networks with Non-fragile Sampled-data Control |
| Authors |
유아연(Liu, Yajuan) ; 이상문(Lee, Sangmoon) |
| DOI |
https://doi.org/10.5370/KIEE.2017.66.1.171 |
| Keywords |
State estimation ; Neural networks ; Time-varying delay ; Non-fragile sampled-data control |
| Abstract |
This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with H_∞ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example. |