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Dynamic Soft-Sensing Model by Combining Diagonal Recurrent Neural Network with Levinson Predictor

  • Hui Geng
  • Zhihua Xiong
  • Shuai Mao
  • Yongmao Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Dynamic soft-sensing model of diesel oil solidifying point (DOSP) in crude distillation unit (CDU) is proposed based on diagonal recurrent neural network (DRNN). Because of long time-delay of the DOSP measurements, multi-step-ahead predictions are obtained recursively by Levinson predictor and then used as input of DRNN. Simulation results on the actual industrial process data show that the proposed dynamic soft-sensing model took good effects practically and significantly diminished the time-delay of output value.

Keywords

Hide Layer Unseen Data Soft Sensor Recurrent Neural Network Model Soft Computing Methodology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hui Geng
    • 1
  • Zhihua Xiong
    • 1
  • Shuai Mao
    • 1
  • Yongmao Xu
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijngP.R. China

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