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., 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
P. Heydari; H. Khaloozadeh; A.P. 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.
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.
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.
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