Paper Info
Title
Prognostics for Aircraft Control Surface Damage Based on Fuzzy Least Squares Support Vector Regression (FLS-SVR).
Abstract
The trends of flight control system state parameters which can be measured are indirect manifestations of surface damage. In order to predict the changes of states trend more accurately, an algorithm based on fuzzy least squares support vector regression (FLS-SVR) was presented. This approach reconstructed the phase space of multivariate time series using K-L transformation method. A FLS-SVR model was built with the new information priority theory according to apply a fuzzy membership to each input point. The SVR parameters were optimized by genetic algorithm (GA) to improve the accuracy of the model. In order to verify the validity of FLS-SVR algorithm, the prognostics and analysis of surface damage trend were performed. The simulation result demonstrates the prognostics model has good predictive ability.
Year
DOI
Venue
2012
10.1007/978-3-642-34390-2_11
Communications in Computer and Information Science
Keywords
Field
DocType
Prognostic,support vector regression,fuzzy membership,least squares,genetic algorithm
Least squares,Prognostics,Multivariate statistics,Fuzzy logic,Support vector machine,Phase space,Control engineering,Control system,Engineering,Genetic algorithm
Conference
Volume
ISSN
Citations 
324
1865-0929
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Lei Dong100.34
Zhang Ren215322.79
Qingdong Li310212.25