基于CEEMD半球谐振陀螺输出预测方法

doi:10.12305/j.issn.1001-506X.2023.10.30

Abstract

Abstract:

In order to solve the real time prediction problem of hemispherical resonator gyro (HRG) output and improve the prediction accuracy of HRG output, a time-series back propagation (BP) neural network output prediction method based on complementary ensemble empirical mode decomposition is proposed. The method uses complementary ensemble empirical mode decomposition to decompose output data of gyro, and then tests the stability of the decomposed signal components. According to the test results, the time-series analysis modeling and BP neural network modeling are selected to model to predict gyro data, and then reconstruct the predicted signal to obtain the final prediction signal.The method overcomes the contradiction between the strong nonlinearity and nonstationarity of HRG output and the requirements of traditional time series analysis modeling. Compared with time series analysis and BP neural network modeling prediction method, the prediction accuracy of the proposed method is improved by 1~2 orders of magnitude, which verifies the effectiveness and accuracy of the proposed method.

Key words: hemispherical resonator gyro (HRG), complementary ensemble empirical mode decom-position (CEEMD), time-series analysis, back propagation (BP) neural network

CLC Number:

U666.1

Zongshou WU, Lixin WANG, Qiang SHEN, Can LI, Wenhua LI. Output prediction method of hemispherical resonator gyro based on CEEMD[J]. Systems Engineering and Electronics, 2023, 45(10): 3259-3264.

Figures/Tables 7

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References 30

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参数	IMF1 ARMA(3, 3)	IMF2 ARMA(3, 3)	IMF3 ARMA(3, 3)	IMF4 ARMA(3, 3)	IMF5 ARMA(3, 3)	IMF6 ARMA(3, 3)	IMF7 ARMA(3, 3)
α₁	1.030 2	-0.950 8	-2.166 5	-2.788 4	-2.952 4	-2.975 8	-2.991 4
α₂	1.013 2	0.784 7	1.872 4	2.679 5	2.919 4	2.953 9	2.982 9
α₃	0.349 8	-0.185 9	-0.572 5	-0.882 4	-0.966 7	-0.978 1	-0.991 9
β₁	0.208 0	0.485 2	1.052 6	1.362 9	1.870 0	1.792 2	2.283 3
β₂	0.456 2	-0.690 3	0.630 2	0.876 2	1.608 0	1.566 9	2.171 5
β₃	-0.144 1	-0.438 5	0.371 8	0.278 7	0.618 5	0.645 8	0.843 7

模型	MSE/V	平均相对预测误差/%
组合模型	5.32×10^-10	1.29
ARMA模型	4.61×10^-8	12.56
BP神经网络模型	2.09×10^-9	2.36

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Output prediction method of hemispherical resonator gyro based on CEEMD

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