@inproceedings{3fdd8966efb14269b66f49e23e2a7943,
title = "Application of piezoelectric gyro's drift compensation algorithm based on neural network",
abstract = "Time serial model (ARMA) and the neural network model based on single temperature input cannot describe well the behaviors of piezoelectric gyro's null drift. So the angle measurement error will not be compensated effectively. A new model based on the three layer error compensated BP neural network considering the temperature and run time input was proposed. The experiment data show that mean variance of the piezoelectric gyro's null drift error is decreased to 0.0128. It is only 8.42\% of uncompensated value. Mean variance of scale factor is decreased to 1.19 {\texttimes} 10-6. This result is only 33.3\% of uncompensated value. And the practicability of this model was proved by the practical measurement.",
keywords = "Compensation, Drift, Neural network, Piezoelectric gyroscope, Scale factor",
author = "Yu Liu and Qiujun Li and Jun Liu and Leilei Li and Youju Mao",
year = "2006",
doi = "10.1109/WCICA.2006.1713300",
language = "English",
isbn = "1424403324",
series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
pages = "4823--4826",
booktitle = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
note = "6th World Congress on Intelligent Control and Automation, WCICA 2006 ; Conference date: 21-06-2006 Through 23-06-2006",
}