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

DOI

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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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  - 10.2991/icsmim-15.2016.114
ID  - Cui2016/01
ER  -