Nonlinear PCA based micro gas sensor array signal processing

Guang Fen Wei, Zhen An Tang, Jun Yu, Philip Ching Ho Chan, Li Ding Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

A nonlinear superposition model was proposed based on the common linear additive model the micro gas sensor array signal processing to improve the precision of quantification and identification. According to the nonlinear model, the Nonlinear Principal Component Analysis (NLPCA) was proposed to process the response signals obtained from a 4 Micro-hotplate (MHP) based gas sensor array. Com-pared with the analyzing results obtained from Principal Component Analysis (PCA), which bases on the linear additive model, the accuracy of gas component identification and concentration quantification are improved greatly.
Original languageEnglish
Pages (from-to)122-126
Number of pages5
JournalGongneng Cailiao yu Qijian Xuebao/Journal of Functional Materials and Devices
Volume11
Issue number1
Publication statusPublished - 1 Mar 2005
Externally publishedYes

Keywords

  • Gas mixture analysis
  • Gas sensor array
  • Nonlinear principal component analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Materials Chemistry
  • Electrical and Electronic Engineering

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