NDLI: On system identification of laser gyroscope static drift

Content Provider	IEEE Xplore Digital Library
Author	Zhu Jiahai Xie Nie Yang Wenjie Li Jun
Copyright Year	2011
Description	Author affiliation: The Engineering College, Air Force Engineering University, Xi'an 710038, China (Zhu Jiahai; Xie Nie; Li Jun) \|\| The First Aviation Academy of Air Force, Xinyang 464000, China (Yang Wenjie)
Abstract	Laser gyroscope as a kind of photoelectric sensor, the static drift output data generally validate to be a non-stationary time series, and traditional Time Series Identification lacks dependable modeling precision. Analyzed the advantages of RBF Neural Network and SVM (Support Vector Machine), combined Wavelet Transform with ANN and SVM, studied the identification methods of WT-RBF (Wavelet Transform RBF Neural Network) and WT-SVM (Wavelet Transform Support Machine), and respectively constructed four kinds of model: RBF, SVM, WT-RBF and WT-SVM. Compared the modeling precision using six performance indexes, the results of computer aided analysis indicates that combined forecasting method can enhance the precision greatly than any other single methods in meeting the performance indexes.
Starting Page	1449
Ending Page	1452
File Size	236349
Page Count	4
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Support vector machines Wavelet transforms Laser Gyroscope Wavelet Transform Neural Network System Identification Artificial neural networks Wavelet analysis Gyroscopes Electronic mail Support Vector Machine
Content Type	Text
Resource Type	Article

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