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Key Words:Markov processes;Applications - Costs - Dynamics - Forecasting - Gyroscopes - Mathematical models - Matrix algebra - Measurements - Probability;Drift - Fibre optic gyroscope - Grey model - Markov chain
Abstract:A grey Markov chain predictive model is discussed to reduce drift influence on the output of fiber optical gyroscopes (FOGs) and to improve FOGs" measurement precision. The proposed method possesses advantages of grey model and Markov chain. It makes good use of dynamic modeling idea of the grey model to predict general trend of original data. Then according to the trend, states are divided so that it can overcome the disadvantage of high computational cost of state transition probability matrix in Markov chain. Moreover, the presented approach expands the applied scope of the grey model and makes it be fit for prediction of random data with bigger fluctuation. The numerical results of real drift data from a certain type FOG verify the effectiveness of the proposed grey Markov chain model powerfully. The Markov chain is also investigated to provide a comparison with the grey Markov chain model. It is shown that the hybrid grey Markov chain prediction model has higher modeling precision than Markov chain itself, which prove this proposed method is very applicable and effective.
Volume:16
Issue:2
Translation or Not:no