Paper Title:
Sensor Dynamic Modeling Based on LS-SVM and NGA
  Abstract

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, Z. G. Feng, K. Shida, "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
$32.00
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