Abstract
The large temperature drift caused by variation of environmental temperature is the main factor affecting the performance of fiber optical gyroscope (FOG). Based the advantages of artificial neural network and the fact that the temperature drift of FOG is a group of multi-variable non-line time series related with temperature, this paper presents modeling temperature drift of fiber optical gyro rate by improved back propagation (BP) training algorithm and by Gauss-Newton training algorithm, comparison between the modeling results of by improved BP algorithm and by gauss-newton algorithm is presented. Modeling results from measured temperature drift data of FOG shows that Gauss-Newton algorithm has higher training precision and shorter convergence time than improved BP algorithm on the same training conditions for application of modeling temperature drift of FOG.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hotate, K.: Fiber Optic Gyros. A Chapter in Optical Fiber Sensors, Vol. IV. Norwood, MA. Artech House, pp. 167–206 (1997)
Lefevre, H.C.: The Fiber Optic Gyroscope. Artech House, Norwood (1993)
Hotate, K.: Future Evolution of Fiber Optic Gyros. In: Proc. SPIE Fiber Optic Gyros.20th Anniversary Conf., Denver, CO., vol. 2837, pp. 33–44 (1996)
Saida, T., Hotate, K.: General Formula Describing Drift of Interferometer Fober-Optic Gyro- due to Faraday Effect Reduction of the Drift in Twin-Depo-I-FOG. Journal of Lightwave Technology 17, 222–228 (1999)
Fengping, Y., Huijuan, L., Shuisheng, J.: Investigation of the Temperature Compensated Method for Fiber Optic Gyros. Acta Optica Sinica 19, 968–974 (1999)
Xiyuan, C.: Modeling Random Gyro Drift by Time Series Neural Networks and by Traditional Method. Proceedings of IEEE Int. Conf. Neural Networks & Signal Processing 1, 810–813 (2003)
Sudhakar, M.P., Weibang, Z.: Modeling Random Gyro Drift Rate by Data Dependent Systems. IEEE Transactions on Aerospace and Electronic Systems 22, 455–459 (1986)
Rong, Z., Yanhua, Z., Qilian, B.: A Novel Intelligent Strategy for Improving Measurement Precision of FOG. IEEE Transactions on Instrumentation and Measurement 49, 1183–1188 (2000)
Hongwei, B., Zhihua, J., Weifeng, T.: A Projection Pursuit Learning Network for Modeling Temperature Drift of FOG. In: Proceedings of IEEE Int. Conf. Neural Networks & Signal Processing, vol. 1, pp. 87–90 (2003)
Daqing, Z.: Study on Application of All-Digital IFOG in New Style Strapdown Attitude and Heading Reference System [D]. Southeast University (2002)
Colin, O.B., Shengchai, C., Tarek, G., Catherine, A.R.: Comparing BP and ART II Neural Network Classifiers for Facility Location. Computers and Engine 28, 43–50 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, X. (2004). Modeling Temperature Drift of FOG by Improved BP Algorithm and by Gauss-Newton Algorithm. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_129
Download citation
DOI: https://doi.org/10.1007/978-3-540-28648-6_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
eBook Packages: Springer Book Archive