An Improved BP Neural Network Algorithm for Evaluating Food Traceability System Performance

DOI

10.2991/meita-15.2015.163How to use a DOI?

Keywords

BP neural network algorithm, Performance evaluation, food traceability system, trigonometric function.

Abstract

BP neural network algorithm has powerful calculation ability, but the algorithm has some shortages such as low convergence which limits its application, so improving BP algorithm has become a matter of concern in the fields related. Based on analyzing improvement methods wildly used today, the paper presents a new BP neural network algorithm and applies it to evaluate food traceability system performance. Firstly, the paper improves the BP algorithm through changing learning rate, trigonometric function to simplify the original calculation structure; secondly, the calculation step of the improved BP algorithm is redesigned to speed up its convergence. Finally, the paper conducts the theoretical analysis of the calculation performance of the improved algorithm and applies it to evaluate food traceability system performance, the theoretical analysis and experimental evaluation results show that the improved algorithm can improve evaluation accuracy and algorithm calculation efficiency and can be used for evaluating food traceability system performance practically.

Copyright

© 2015, 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  - Weiya Guo
PY  - 2015/08
DA  - 2015/08
TI  - An Improved BP Neural Network Algorithm for Evaluating Food Traceability System Performance
BT  - Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications
PB  - Atlantis Press
SP  - 876
EP  - 879
SN  - 2352-5401
UR  - https://doi.org/10.2991/meita-15.2015.163
DO  - 10.2991/meita-15.2015.163
ID  - Guo2015/08
ER  -