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IA-BP optimal algorithm in measuring tobacco moisture content with resonant microwave sensor

  • Yu Jiang*
  • , Jun Cao
  • , Jiu Qing Liu
  • , Guo Hui Yang
  • *Corresponding author for this work
  • Northeast Forestry University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

BP algorithm has been widely used in calibrating measurement results detected by microwave resonator for improvement of accuracy. Conventional BP algorithm tends to get into infinitesimal locally, which worsens the stability of the measurement accuracy. An evolutionary neural network model based on IA-BP optimal algorithm is proposed in this paper. In the model, IA algorithm is first used for global search and then BP algorithm for local search. Experiments indicated that the IA-BP optimal algorithm effectively avoid getting into infinitesimal locally and has the merits of high prediction precision, rapid convergence, global superiority and accuracy for optimization, which improves the measurement accuracy with the mean squared error 0.0125, the mean absolute error 0.0715, the mean relative error 0.1186 and the certain coefficient 0.9965 between the predicted moisture content and the real value.

Original languageEnglish
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages4744-4748
Number of pages5
StatePublished - 2005
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: 18 Aug 200521 Aug 2005

Publication series

Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

ConferenceInternational Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/TerritoryChina
CityGuangzhou
Period18/08/0521/08/05

Keywords

  • Evolutionary neural network
  • IA-BP optimal algorithm
  • Moisture content measurement
  • Open resonant microwave moisture sensors

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