PropertyValue
?:abstract
  • In order to overcome the long time delays and dynamic complexity in industrial sintering process, a modeling method of prediction of burn-through point (BTP) was proposed based on support vector machines (SVMs). The results indicate SVMs outperform the three-layer Backpropagation (BP) neural network in predicting burn-through point with better generalization performance, and are satisfactory. The model can be used as plant model for the burn-through point control of on-strand sinter machines. ()
?:appearsInConferenceSeries
?:citationCount
  • 2 ()
is ?:cites of
?:cites
?:created
  • 2016-06-24 ()
?:creator
?:doi
  • 10.1007/978-3-540-37256-1_88 ()
?:endingPage
  • 730 ()
?:estimatedCitationCount
  • 2 ()
is ?:hasCitingEntity of
?:hasDiscipline
?:hasURL
?:language
  • en ()
?:publicationDate
  • 2006-01-01 ()
?:publisher
  • Springer Berlin Heidelberg ()
?:rank
  • 21948 ()
?:referenceCount
  • 8 ()
?:startingPage
  • 722 ()
?:title
  • Prediction of Sinter Burn-Through Point Based on Support Vector Machines ()
?:type

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