Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

Network Calibration interval optimization algorithm based on NLGA-GM (1, 1)

Authors
Xinchen Cui, Zhenlin Chen
Corresponding Author
Xinchen Cui
Available Online January 2016.
DOI
https://doi.org/10.2991/icsmim-15.2016.114How to use a DOI?
Keywords
Genetic algorithm, gray model, nonlinear programming, calibration interval.
Abstract

In order to optimize the calibration intervals, predict its calibration data using gray GM (1, 1) model. For lack of modeling parameter vector solving method, use genetic algorithms to seek optimal parameter vector; use nonlinear programming function’s good ability of local searching to improve GA; finally, improve the initial base value selection of GM (1, 1) model. NLGA-GM (1, 1) forecasting model is given to optimize the calibration interval, and the optimizing model is validated through experiments. The results show that the improved model is better than the traditional GM at structure, data fitting accurateness and the calibration interval forecasts requirements.

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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Volume Title
Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
Series
Advances in Computer Science Research
Publication Date
January 2016
ISBN
978-94-6252-157-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icsmim-15.2016.114How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Xinchen Cui
AU  - Zhenlin Chen
PY  - 2016/01
DA  - 2016/01
TI  - Network Calibration interval optimization algorithm based on NLGA-GM (1, 1)
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 614
EP  - 618
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.114
DO  - https://doi.org/10.2991/icsmim-15.2016.114
ID  - Cui2016/01
ER  -