EMD based fiber-optic gyroscope temperature drift multi-scale extreme learning machine training method

Abstract

The invention discloses an EMD based fiber-optic gyroscope temperature drift multi-scale extreme learning machine training method. The EMD based fiber-optic gyroscope temperature drift multi-scale extreme learning machine training method comprise the following steps of (1) adopting a bounded ensemble empirical mode decomposition (EEMD) method to respectively decompose drifting output data of a fiber-optic gyroscope in different temperature-changing-rate environments into a series of intrinsic mode functions; (2) adopting a sample entropy (SE) measurement theory to calculate SE values of the intrinsic mode functions (IMF) in the step (1); (3) determining an IMF set led by noise and an IMF set having different self-similarity features according to the fluctuation trend and sizes of the SE values; (4) superposing the IMF sets determined in the step (3) and having the similar self-similarity features to serve as ELM model training inputs, using temperature gradients at the temperature change rates corresponding to the group of output data as another input training ELM model, similarly, using different superposed self-similarity IMF and corresponding temperature gradients to generate different ELM models through training; (5) accumulating the multiple ELM models generated in the step (4) to obtain a final integrated multi-scale model.

Description

technical field [0001] The invention relates to an EMD-based multi-scale extreme learning machine training method for fiber optic gyroscope temperature drift, which belongs to the field of modeling compensation of inertial devices, and can also be used for modeling other error signals with non-stationary characteristics. Background technique [0002] The interferometric fiber optic gyroscope (IFOG) is greatly affected by the ambient temperature, and its constantly changing temperature field inside the fiber ring leads to constant changes in the thermal expansion coefficient and refractive index of the fiber material, and these changes are anisotropic at different positions of the fiber ring , resulting in thermally induced non-reciprocal phase shift errors. It is very difficult to improve the accuracy of medium and high-precision fiber optic gyroscopes in terms of mechanism. In engineering, methods such as improving fiber winding technology, adding temperature control equipm...

AI Technical Summary

Problems solved by technology

Non-stationary modeling methods based on time series analysis, such as the autoregressive differential moving average (ARIMA) model, the modeling process is complex, and the ability to approximate complex nonlinearities is limited

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