TY - JOUR ID - 51573 TI - Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods JO - Journal of Aerospace Science and Technology JA - JAST LA - en SN - 1735-2134 AU - Heydari, P. AU - Khaloozadeh, H. AU - Heydari, A.P. AD - Y1 - 2009 PY - 2009 VL - 6 IS - 1 SP - 35 EP - 44 KW - Dynamically Tuned Gyroscope KW - Random Drift KW - Time series methods KW - Neural-Networks KW - Fuzzy logic KW - Error compensation DO - N2 - 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 UR - https://jast.ias.ir/article_51573.html L1 - https://jast.ias.ir/article_51573_429d9d4e1b16bff349b16aa971be8b7b.pdf ER -