Comparison of PCA and Model Optimization Algorithms for System
Identification Using Limited Data
Abstract:
Support Vector Machine (SVM) is a novel modeling method that
is valuable in regression and classification. Kernel parameters setting in the
SVM training process, along with the feature selection, significantly affects
system identification accuracy. The objective of this study is to obtain the
better algorithms for better prediction accuracy. This study develops Principle
Component Analysis (PCA) for feature selection and a grid searching and k-fold
cross validation (GSCV) approach for parameter optimization in the SVM. Numerical
and engineering results indicate that SVM based on PCA can be used for identification
of nonlinear functions with related input variables, while the SVM based on
GSCV is useful for complex system identification with limited number with kinds
of uncertainties.
How to cite this article
He Qinshu, Liu Xinen and Xiao Shifu, 2013. Comparison of PCA and Model Optimization Algorithms for System
Identification Using Limited Data. Journal of Applied Sciences, 13: 2082-2086.
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