@inproceedings{c80bad8fcbaa4f37a59bd1f7c48be670,
title = "A new method for gyroscope fault diagnosis based on CGA RBFNN and multi-wavelet entropy",
abstract = "An original method based on CGA (Genetic Algorithm based on Cloud-model) RBFNN (Radial Basis Function Neural Network) is proposed for the online fault diagnosis of gyroscope. Based on the information entropy and wavelet transform theory, wavelet energy entropy (WEE) and wavelet time entropy (WTE) are extracted to be the input of RBFNN. Besides, CGA is used to optimize the parameters of RBFNN. The simulation results show that this new method can reach an efficient search for global convergence, and prevent it from similar problem of partial efficiency in traditional genetic algorithm (TGA). After trained, the RBFNN can accomplish the online fault diagnosis of gyroscope accurately and quickly.",
keywords = "CGA, Fault diagnosis, Gyroscope, RBFNN, Wavelet energy entropy (WEE), Wavelet time entropy (WTE)",
author = "Ji Yu and Deyun Zhou and Peng He and Jichuan Huang",
note = "Publisher Copyright: {\textcopyright} 2013 IEEE.; 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer, MEC 2013 ; Conference date: 20-12-2013 Through 22-12-2013",
year = "2013",
doi = "10.1109/MEC.2013.6885047",
language = "英语",
series = "Proceedings - 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer, MEC 2013",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "39--43",
booktitle = "Proceedings - 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer, MEC 2013",
}