Application of fuzzy neural network for fog zero point drift modeling

ICIC Express Letters

Volumn 3, Issue 3, 2009, Pages 847-852

  Chen, Xiyuan ^a   Shen, Chong ^a   Xu, Changyan ^a  

^a SOUTHEAST UNIVERSITY   (China)

Author keywords

FLP algorithm; Fuzzy system; RBF neural network; Zero point drift of FOG

Indexed keywords

FLP ALGORITHM; RBF NEURAL NETWORK; ZERO POINT; ZERO POINT DRIFT OF FOG;

ALGORITHMS; COMPUTER SIMULATION; FOG; FUZZY NEURAL NETWORKS; FUZZY SETS; GYROSCOPES; RADIAL BASIS FUNCTION NETWORKS;

FUZZY LOGIC;

EID: 77953905420     PISSN: 1881803X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (10)

1. G. Malykin, and V. Pozdnyakova, Mathematical modeling of random couplingbetween polarization modes in single-mode optical fibers: VIII, zero drift in afiber ring interferometer with a white light source, Optics and Spectroscopy,Vol. 94, No. 2, pp. 300-311, 2003.
2. Y. Gu, Z. Liang and D. Li, Polarization error for fiber optic gyroscopes,Proc. of the Third International Symposium, on Instrumentation Science andTechnology, Vol. 2, pp. 676-680, 2004.
3. J. Lai, J. Liu and S. Sheng, Improved learning rule of RBFNN foridentifying temperature-introduced drift of IFOG, Journal of SoutheastUniversity, Vol. 36, No. 4, pp. 537-541, 2006 (in Chinese).
4. H. Medjadba, S. Lecler and L. Simohamed, et al., Modeling a multimodeSagnac interferometer: Application for an embarked fiber optic gyroscope, Proc.of SPIE on Optical Sensors, Strasbourg, France, Vol. 7003, pp. 3101-3108, 2008.
5. X. Chen, Modeling temperature drift of FOG by improved BP algorithm andby Gauss-Newton algorithm, Lecture Notes in Computer Science, Vol. 3174, No. 2,pp. 805-812, 2004.
6. M. David, S. Nathan and R. Kathleen, et al., A practical approximationalgorithm for the LMS line estimator, Computational Statistics & DataAnalysis, Vol. 51, No. 5, pp. 2461-2486, 2007.
7. T. Ioannis, G. Dimitris and G. Euripidis, Neural network construction andtraining using grammatical evolution, Neurocomputing, Vol. 72, no. 1-3, pp.269-277, 2008.
8. G. Markos, P. Themis and I. Dimitrios, A methodology for automated fuzzymodel generation, Fuzzy Sets and Systems, Vol. 159, No. 23, pp. 3201-3220, 2008.
9. M. Paul, and B. Hector, Supervisory fuzzy control for networked controlsystems, ICIC Express Letters, Vol. 3, No. 2, pp. 233-240, 2009.
10. Y. Wang, Fuzzy clusttering analysis by using genetic algorithm, ICICExpress Letters, Vol. 2, No. 4, pp. 331-337, 2008.