A Levinson Predictor Based Compensatory Fuzzy Neural Network and Its Application in Crude Oil Distillation Process Modeling

  • Yongfeng He
  • Quanyi Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Levinson predictor based Compensatory fuzzy neural networks (LPCFNN), which can be trained by a back-propagation learning algorithm, is proposed as a modeling technique for crude oil distillation processes. This approach adds feedback to the input by using Levinson predictor. Simulation experiments are made by applying proposed LPCFNN on modeling for crude oil distillation process to confirm its effectiveness.


Membership Function Simulation Error Rule Layer Step Step Output Action Strength 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yongfeng He
    • 1
  • Quanyi Fan
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina

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