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Xiaotong Wang, Chih-Chen Chang and Fang Du
Journal Article
Ground-level ozone forecasting using data-driven methods
T. A. Solaiman, P. Coulibaly and P. Kanaroglou
Air Quality, Atmosphere & Health, 2008, Volume 1, Number 4, Pages 179-193
Book Chapter
Actuators in Adaptronics
Hartmut Janocha, Ronan Leletty, Frank Claeyssen, Goran Engdahl and Jürgen Hesselbach, et al.
2007, Adaptronics and Smart Structures, Pages 95-300
A new semiactive nonlinear adaptive controller for structures using MR damper: Design and experimental validation
Saban Cetin, Erkan Zergeroglu, Selim Sivrioglu and Ismail Yuksek
Nonlinear Dynamics, 2011, Volume 66, Number 4, Pages 731-743
The Hysteresis Bouc-Wen Model, a Survey
Mohammed Ismail, Fayçal Ikhouane and José Rodellar
Archives of Computational Methods in Engineering, 2009, Volume 16, Number 2, Pages 161-188
Sensitivity control of a MR-damper semi-active suspension
Arjon Turnip, Seonghun Park and Keum-Shik Hong
International Journal of Precision Engineering and Manufacturing, 2010, Volume 11, Number 2, Pages 209-218
Semi-active control of a cable-stayed bridge under multiple-support excitations
Dai Ze-bing, Huang Jin-zhi and Wang Hong-xia
Journal of Zhejiang University - Science A, 2004, Volume 5, Number 3, Pages 317-325
Analysis of magnetorheological fluid damper
Yan Yang, Hui Li and Bo-seon Kang
Journal of Central South University of Technology, 2007, Volume 14, Supplement 1, Pages 263-265
Modeling of a magneto-rheological (MR) fluid damper using a self tuning fuzzy mechanism
Kyoung Kwan Ahn, Dinh Quang Truong and Muhammad Aminul Islam
Journal of Mechanical Science and Technology, 2009, Volume 23, Number 5, Pages 1485-1499
Semi-Active Suspensions
Mechanical Engineering Series, 2006, Vehicle Dynamics and Control, Pages 357-385
Adaptive Fuzzy Neural Network Control for Transient Dynamics of Magneto-rheological Suspension with Time-Delay
Xiaomin Dong, Miao Yu, Changrong Liao, Weimin Chen and Honghui Zhang, et al.
Lecture Notes in Computer Science, 2006, Volume 3972, Advances in Neural Networks - ISNN 2006, Pages 1046-1051
Key words: Control; Magnetorheological fluid damper; Neural network; Noise; Sensitivity analysis; Signal
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