Application of LS-SVM by GA for Reducing Cross-Sensitivity of Sensors

Wen-bin Zhang (), Jing-ling Chen, Chun-guang Suo and Wen-sheng Gui
Additional contact information
Wen-bin Zhang: Kunming University of Science and Technology
Jing-ling Chen: Kunming University of Science and Technology
Chun-guang Suo: Kunming University of Science and Technology
Wen-sheng Gui: Kunming University of Science and Technology

Chapter Chapter 71 in Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012), 2013, pp 711-720 from Springer

Abstract: Abstract Least square support vector machine (LS-SVM) is widely used in the regression analysis, but the prediction accuracy greatly depends on the parameters selection. In this paper, Simple Genetic Algorithm is applied to optimize the LS-SVM parameters; correspondingly, the prediction accuracy is improved. Sensors are always sensitive to several parameters, and this phenomenon is called cross-sensitivity which restricts the application of sensors in engineering. In order to reduce cross-sensitivity, the model of multi-sensor system measurement is established in this paper. For solving the nonlinear problems in the model, LS-SVM is used to establish the inverse model. It proves that the method has a high forecasting precision. It is beneficial to the application of sensors.

Keywords: Multi-sensorsystem; Nonlinear system; Simple genetic algorithm; LS-SVM (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-33012-4_71

Ordering information: This item can be ordered from
http://www.springer.com/9783642330124

DOI: 10.1007/978-3-642-33012-4_71

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().