Description: In order to improve accuracy, to study a particular model of the MEMS gyroscope random drift model. Using run-length analysis of the test gyro random drift data stationarity, and in accordance with the drift for the average non-stationary, the variance of the random process a smooth conclusion, the use of gradient radial basis (RBF) neural network drift data to build mode. The experimental results show that: compared to the classical RBF network model, this method of establishing a model to better describe the MEMS gyroscope drift special compared with the seasonal time series model, the effect of their compensation increased by approximately 15.
- [kalmanfilt] - AR (2) model for data modeling, further
- [kmeans_1] - RBF neural network algorithm mean K, C p
- [trace_generate] - inertial navigation study the trajectory
- [jielianguandao] - SINS navigation simulation program, incl
- [rbf1] - This source code is used MATLAB training
- [matlabtosolve] - err
- [RBF_s] - RBF neural network, using gradient desce
- [suij] - Generated using Matlab to meet the condi
- [bp] - BP neural network with the initial level
- [ss] - Neural Networks in Stock Forecasting Res
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