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A Model Predictive Control of a Grain Dryer with Four Stages Based on Recurrent Fuzzy Neural Network

  • Chunyu Zhao
  • Qinglei Chi
  • Lei Wang
  • Bangchun Wen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4491)

Abstract

This paper proposes a model predictive control scheme with recurrent fuzzy neural network (RFNN) by using the temperature of the drying process for grain dryers. In this scheme, there are two RFNNs and two PI controllers. One RFNN with feedforeward and feedback connections of grain layer history position states predicts outlet moisture content (MPRFNN), and the other predicts the discharge rate of the dryer (RPRFNN). One PI controller adjusts the objective of the discharge rate by using MPRFNN, and the other adjusts the given frequency of the discharge motor to control the discharge rate of the grain dryer to reach its objective by using RPRFNN. The experiment is carried out by applying the proposed scheme on the control of a gain dryer with four stages to confirm its effectiveness.

Keywords

Discharge Rate Model Predictive Control Maize Kernel Feedback Connection Gaussian Membership Function 
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 2007

Authors and Affiliations

  • Chunyu Zhao
    • 1
  • Qinglei Chi
    • 1
  • Lei Wang
    • 2
  • Bangchun Wen
    • 1
  1. 1.School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004P.R. China
  2. 2.Shenyang Neusoft Software Co.ltd, Shenyang 110179P.R. China

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