@inproceedings{731f11f6d194495d8ad8deae206f9192,
title = "Fault diagnosis of hydraulic system of quadruped robot by SVM based on rough set and CS algorithm",
abstract = "For the fault diagnosis of hydraulic system of quadruped robot, an optimized support vector machine learning method based on rough set and Cuckoo Search algorithm is proposed. Firstly, the attributes of samples are reduced by the rough set to decrease the dimensions and eliminate the redundant information. Then, the parameters of support vector machine are optimized by Cuckoo Search algorithm, this algorithm imitates the obligate brood parasitism of the cuckoo species. Finally, the support vector machine classifier is established. The simulation results show that when diagnosing the faults of the hydraulic system of quadruped robot, the proposed method can shorten the training time as well as improve the classification accuracy.",
keywords = "Cuckoo Search, Fault Diagnosis, Hydraulic System, Rough Set, SVM",
author = "Liling Ma and Jiali Zhao and Junzheng Wang and Shoukun Wang",
note = "Publisher Copyright: {\textcopyright} 2015 Technical Committee on Control Theory, Chinese Association of Automation.; 34th Chinese Control Conference, CCC 2015 ; Conference date: 28-07-2015 Through 30-07-2015",
year = "2015",
month = sep,
day = "11",
doi = "10.1109/ChiCC.2015.7260622",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6264--6268",
editor = "Qianchuan Zhao and Shirong Liu",
booktitle = "Proceedings of the 34th Chinese Control Conference, CCC 2015",
address = "United States",
}