Neural network signal detection of an SO2 electrode

Mei Jywan Syu, Jwo Ying Liu

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


A novel SO2 electrode made from polyaniline/Nafion membrane was developed. The electrode was designed to detect SO2 content at ppm levels from the exhaust gas of factories. An SO2 concentration of 20-250 ppm was measured through a response current and a neural network was introduced to learn the response signal and predict the SO2 concentration. By providing the whole pattern of the response currents at different sampling times as input nodes of the network, the corresponding SO2 concentration can be learned and predicted as the output of the network. Such a parallel approach is different from the conventional calibration method. The latter takes 6 min to reach a steady response. Neural network signal pattern detection does not necessarily require a steady response, and as a result, the response time could be reduced to 3 min. The amplified signal in the conventional calibration method often requires a noise filter which is not necessary for the neural network approach because of its fault tolerance ability. A saturation-type transfer function bx/(1+|x|) was chosen and excellent results were obtained. Response currents acquired at different sampling intervals corresponding to different numbers of input nodes, were compared by examining the training and prediction ability of the neural network. The minimum number of training data for accurate predictions was also determined.

Original languageEnglish
Pages (from-to)186-194
Number of pages9
JournalSensors and Actuators, B: Chemical
Volume49 B49
Issue number3
Publication statusPublished - 1998

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Electrical and Electronic Engineering
  • Materials Chemistry


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