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Prediction of Drying Indices for Paddy Rice in a Deep Fixed-Bed Based on Neural Network

  • Danyang Wang
  • Chenghua LiEmail author
  • Benhua Zhang
  • Ling Tong
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 509)

Abstract

In this study, four artificial neural network models are developed for paddy rice drying in a deep fixed-bed to predict five drying performance indices, including additional crack percentage, drying moisture uniformity, energy efficiency rate, germinating percentage and drying time. The four neural networks are BP, RBF, GRNN and ELMAN. After plenty of trials with a variety of neural network architectures, neural network with five inputs and five outputs is better than network with five inputs and any other outputs. Five drying parameters including paddy original moisture content, air temperature, air velocity, paddy thickness and tempering time are regarded as input vectors of the neural networks. The experimental results show that neural networks have good performance in predicting the paddy drying process. And also, the simulation indicate that the RBF neural network has advantages over other three neural networks in performance.

Keywords

Neural network Prediction Drying indices Paddy rice Deep fixed-bed drying Drying parameters 

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Danyang Wang
    • 1
  • Chenghua Li
    • 2
    Email author
  • Benhua Zhang
    • 1
  • Ling Tong
    • 1
  1. 1.College of EngineeringShenyang Agricultural UniversityShenyangChina
  2. 2.College of Mechanical EngineeringShenyang Ligong UniversityShenyangChina

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