![](/images/graphics-bg.png)
Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm
Joint Authors
Chen, Xiyuan
Shen, Chong
Song, Rui
Zhang, Hong
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-04
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Based on the temperature drift characteristic of fiber optic gyroscope (FOG), a novel modeling and compensation method which integrated the artificial fish swarm algorithm (AFSA) and back-propagation (BP) neural network is proposed to improve the output accuracy of FOG and the precision of inertial navigation system.
In this paper, AFSA is used to optimize the weights and threshold of BP neural network which determine precision of the model directly.
In order to verify the effectiveness of the proposed algorithm, the predicted results of BP optimized by genetic algorithm (GA) and AFSA are compared and a quantitative evaluation of compensation results is analyzed by Allan variance.
The comparison result illustrated the main error sources and the sinusoidal noises in the FOG output signal are reduced by about 50%.
Therefore, the proposed modeling method can be used to improve the FOG precision.
American Psychological Association (APA)
Song, Rui& Chen, Xiyuan& Shen, Chong& Zhang, Hong. 2014. Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm. Journal of Sensors،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1042919
Modern Language Association (MLA)
Song, Rui…[et al.]. Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm. Journal of Sensors No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1042919
American Medical Association (AMA)
Song, Rui& Chen, Xiyuan& Shen, Chong& Zhang, Hong. Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm. Journal of Sensors. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1042919
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1042919