Paper Title:
Component Spectrum Recognition for Mixed Gas Based on SVM
  Abstract

As for the problem that component gas characteristic spectrum lines overlaps seriously in the identification of Mixed Gas, Support Vector Machine is introduced for the identification, and an one-by-one identification methods for Mixed Gas classification based on the binary category identification model based on the support vector machine is proposed in this article. One-by-one category identification is carried out for each mixed gas when the characteristic spectrum lines are overlapped seriously and is transformed in high dimensional space into linear by SVM kernel function transformation. In the experiment for gas component identification of a natural gas, we compare the recognition results affected by different kernel functions, data preprocessing, feature extraction, numbers of training samples and other conditions. The results show that the method has the correct recognition rate of over 97% for the natural gas whose concentration is over 1%, and it has a great promotional value both in theory and practical application.

  Info
Periodical
Chapter
Chapter 3: Sensor, Test and Signal Processing
Edited by
Zhixiang Hou
Pages
557-560
DOI
10.4028/www.scientific.net/AMM.128-129.557
Citation
P. Bai, J. Z. Ji, P. Liu, D. T. Geng, "Component Spectrum Recognition for Mixed Gas Based on SVM", Applied Mechanics and Materials, Vols. 128-129, pp. 557-560, 2012
Online since
October 2011
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Price
$32.00
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