Title: A Dynamic Compensation Method for Sensors Based on Least Squares Support Vector Machines
Abstract: The standard support vector machine(SVM) and least squares support vector machine(LS-SVM) was compared and described. On the basis of the compared results,a novel method using LS-SVM model to correct dynamic measurement errors of sensors is developed.The design steps and learning algorithm are also addressed.Compared with standard SVM compensation methods,this method possesses prominent advantages: the constraints of inequalities in the standard SVM approach are replaced by equality-type constraints in LS-SVM and the LS-SVM solution follows directly from solving a set of linear equations instead of quadratic programming.In the same condition,the speed of construction is 10~10~2 times than that of the standard SVM method.The results showed that the error of compensation is about 40% of the SVM method.As a result,the dynamic compensation method of LS-SVM is faster in speed,higher in accuracy,much capability of noise resistance and better for sensors dynamic system.
Publication Year: 2007
Publication Date: 2007-01-01
Language: en
Type: article
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