Application of Improved SOM Neural Network in Manufacturing Process Quality Control

DOI

10.2991/iccsee.2013.290How to use a DOI?

Keywords

Self-organizing Mapping (SOM), artificial neural network, manufacturing process quality control

Abstract

The use of neural networks in quality control has been a popular research topic over the last decade. An adaptive self-organizing mapping (SOM) neural network algorithm is proposed to overcome the shortages of traditional neural networks in this paper. In order to improve the classification effectiveness of SOM neural network, this paper designs an improved SOM neural network, which improved the algorithm formula based on input vector, the number setting of competitive layer neurons and the initializing weight vector. And the method is used to classify the product of cement slide shoe bearing in manufacturing process quality control, and experiment results show that the algorithm adapts well the unsupervised learning problems.

Copyright

© 2013, 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  - Yibing Li
AU  - Fei Pan
PY  - 2013/03
DA  - 2013/03
TI  - Application of Improved SOM Neural Network in Manufacturing Process Quality Control
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1154
EP  - 1157
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.290
DO  - 10.2991/iccsee.2013.290
ID  - Li2013/03
ER  -