Neural network-based method for anti-glare glass preparation process parameters optimization

DOI

10.2991/nceece-15.2016.81How to use a DOI?

Keywords

Neural network; Parameter optimization; Anti-glare glass

Abstract

Along with the urban development, the application of anti-glare glass is more and more widely, high transmittance and low reflection of glass manufacturing technology research is of great significance. Because of the complexity of the anti-glare glass preparation technology, anti-glare glass transmittance is affected by multiple factors. Because of complexity nonlinear relation between the real production data, response surface method can't solve the problem of anti-glare glass preparation process parameters optimization. The BP neural network is proposed in this paper to structure the complex nonlinear model between the design vector with the response vector. BP neural network has high learning and representation ability and have ability by establishing a good mapping model. Using BP neural network model of high generalization ability on the optimal parameter combination optimization search about the corrosion condition of glass, with less test data to get the ideal parameter design.

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  - Jianli Yu
AU  - Hongqi Huang
AU  - Xiaojuan Niu
PY  - 2015/12
DA  - 2015/12
TI  - Neural network-based method for anti-glare glass preparation process parameters optimization
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 413
EP  - 417
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.81
DO  - 10.2991/nceece-15.2016.81
ID  - Yu2015/12
ER  -