Network Calibration interval optimization algorithm based on NLGA-GM (1, 1)
- https://doi.org/10.2991/icsmim-15.2016.114How to use a DOI?
- Genetic algorithm, gray model, nonlinear programming, calibration interval.
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.
- © 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 -