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Indexed by:期刊论文

Date of Publication:2011-01-01

Journal:International Journal of Mechatronics and Automation

Included Journals:Scopus

Volume:1

Issue:2

Page Number:71-78

ISSN No.:20451059

Abstract:This paper presents the results of a research into the application of principal component analysis (PCA) for the mechanical coupling system modelling based on support vector machine (SVM). Because of the impact of multiple geometric parameters, there are more input variables in the mechanical coupling system modelling process. The high-dimensional data poses an interesting challenge to machine learning, as the presence of high numbers of redundant or highly correlated variables can seriously degrade modelling accuracy. In this study, we use PCA as the preprocessor for mechanical coupling system modelling, so as to realise dimension reduction of the high-dimensional data and improve the predictive performance of machine learning method, and then SVM is used for mechanical coupling system modelling. Experiments are carried out on a typical mechanical coupling, hydraulic valve. The results show that the use of PCA method can improve the performance of machine learning method in the modelling of highdimensional data. ? 2011 Inderscience Enterprises Ltd.