Sensor Dynamic Modeling Based on LS-SVM and NGA

Abstract:

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Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

Info:

Periodical:

Key Engineering Materials (Volumes 381-382)

Edited by:

Wei Gao, Yasuhiro Takaya, Yongsheng Gao and Michael Krystek

Pages:

439-442

DOI:

10.4028/www.scientific.net/KEM.381-382.439

Citation:

Q. Wang et al., "Sensor Dynamic Modeling Based on LS-SVM and NGA", Key Engineering Materials, Vols. 381-382, pp. 439-442, 2008

Online since:

June 2008

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Price:

$35.00

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