Quality Prediction Model Based on Variable-Learning-Rate Neural Networks in Tobacco Redrying Process

DOI

10.2991/icmia-16.2016.139How to use a DOI?

Keywords

Tobacco; Drying Process; Back Propagation Neural Network; Variable Learning Rate; Quality Prediction Model

Abstract

This paper presents an innovative method with the variable-learning-rate-based back propagation neural network (BPNN) for establishing a quality prediction model of tobacco redrying process. First, characteristics of the process and correlation of the process variables are analyzed, and eight input parameters and two output quality indicators of the model are determined. Second, a quality prediction model of the tobacco redrying process is established by using the BPNN structure. In the process of network training, BP algorithm is improved by using the method of variable learning rate, and satisfactory prediction results are obtained. Finally, in order to verify the effectiveness of this method, the improved BPNN model is applied for simulation experiment, and is compared with ordinary BPNN. The prediction results show that the improved model possesses strong self-learning function and higher prediction accuracy.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiankang Yin
AU  - Changhua Chen
PY  - 2016/11
DA  - 2016/11
TI  - Quality Prediction Model Based on Variable-Learning-Rate Neural Networks in Tobacco Redrying Process
BT  - Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-16.2016.139
DO  - 10.2991/icmia-16.2016.139
ID  - Yin2016/11
ER  -