In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages are discussed
Heydari,P. , Khaloozadeh,H. and Heydari,A. (2009). Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods. Journal of Aerospace Science and Technology, 6(1), 35-44.
MLA
Heydari,P. , , Khaloozadeh,H. , and Heydari,A. . "Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods", Journal of Aerospace Science and Technology, 6, 1, 2009, 35-44.
HARVARD
Heydari P., Khaloozadeh H., Heydari A. (2009). 'Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods', Journal of Aerospace Science and Technology, 6(1), pp. 35-44.
CHICAGO
P. Heydari, H. Khaloozadeh and A. Heydari, "Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods," Journal of Aerospace Science and Technology, 6 1 (2009): 35-44,
VANCOUVER
Heydari P., Khaloozadeh H., Heydari A. Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods. Journal of Aerospace Science and Technology, 2009; 6(1): 35-44.
