Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods

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Abstract:

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

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Journal title

Journal of Aerospace Science and Technology

volume 6 issue 1

pages 35- 44

publication date 2630-04-21

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Keywords

Dynamically Tuned Gyroscope Random Drift Time series methods Neural-Networks Fuzzy Logic Error compensation

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