Optic fiber gyroscope random drift modeling method based on locally variable integrated neural network

A neural network model and neural network technology, applied in the field of random drift error modeling and fiber optic gyroscope, can solve the problems of poor generalization ability and low applicability of modeling system, achieve low computational cost, improve overall generalization ability and Effects of prediction accuracy and high modeling accuracy

Active Publication Date: 2010-09-22
SOUTHEAST UNIV
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Problems solved by technology

[0005] However, the traditional software method only realizes the modeling of the fiber optic gyroscope at a certain static temperature, and the modeling accurac

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  • Optic fiber gyroscope random drift modeling method based on locally variable integrated neural network
  • Optic fiber gyroscope random drift modeling method based on locally variable integrated neural network
  • Optic fiber gyroscope random drift modeling method based on locally variable integrated neural network

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Embodiment Construction

[0031] The realization process of the fiber optic gyroscope random drift modeling method of the present invention is as follows figure 1 As shown, it mainly includes the following four steps:

[0032] (1) Establish an integrated neural network model

[0033] like figure 2 As shown, D is all training samples, D i (i=1, 2) are the training samples obtained after filtering by different filters, φ i (x)(i=1, 2) is the learning sample D i The sub-neural network obtained after training, R is the variable integration weight matrix, φ(x) is the integrated neural network model obtained after integrating each sub-network. In the integrated neural network strategy, each sub-network is obtained by training with different sample sets, and then the final prediction model is integrated through the data fusion module. In the present invention, the integrated neural network makes full use of the difference of the optical fiber gyroscope data filtered by different filters, which greatly i...

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Abstract

The invention relates to an optic fiber gyroscope random drift modeling method based on a locally variable integrated neural network. The method comprises the following steps: (1) establishing an integrated neural network model; (2) obtaining a learning sample; (3) training the integrated neural network; and (4) modeling to the random drift of an optic fiber gyroscope. The method firstly uses theFLP/wavelet filtering method to denoise the original output signals of the optic fiber gyroscope and form a data pretreatment system with good denoising effect and wide application range; secondly the locally variable integrated neural network is used in modeling, the integrated neural network overfits the training samples, the defects of low precision and poor stability of the sub-neural networkare overcome; the locally variable weight method based on temperature is introduced to ensure that the network can adjust the integrated weight matrix according to the change of temperature; and the optimum network modeling precision can be ensured at different temperatures, the generalization ability of network is increased, and the dynamic modeling of optic fiber gyroscope random drift is realized.

Description

technical field [0001] The invention belongs to the technical field of inertia, relates to an optical fiber gyroscope, in particular to a random drift error modeling method of an interference type optical fiber gyroscope, and is applicable to various optical fiber gyroscopes. Background technique [0002] The fiber optic gyroscope has extremely high civil and military value, and has been highly valued by the state in its development. Since the 1980s, the state has gradually increased its investment in this research. The fiber optic gyroscope is a measuring instrument based on the Sagnac effect, which uses a solid-state all-fiber structure to measure the angular velocity of the carrier's rotation. Compared with traditional mechanical gyroscopes, it has many advantages, such as high precision, impact resistance, good shock resistance, large dynamic range, and insensitivity to gravitational acceleration, etc. However, there are still some shortcomings in the fiber optic gyrosc...

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Application Information

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IPC IPC(8): G06N3/08G01C19/72
Inventor 陈熙源申冲
Owner SOUTHEAST UNIV
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