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Sensors 2014, 14(10), 18711-18727;

Laser Gyro Temperature Compensation Using Modified RBFNN

College of Automation, Harbin Engineering University, Harbin 150001, China
Author to whom correspondence should be addressed.
Received: 9 July 2014 / Revised: 2 September 2014 / Accepted: 16 September 2014 / Published: 9 October 2014
(This article belongs to the Special Issue Optical Gyroscopes and Navigation Systems)
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To overcome the effect of temperature on laser gyro zero bias and to stabilize the laser gyro output, this study proposes a modified radial basis function neural network (RBFNN) based on a Kohonen network and an orthogonal least squares (OLS) algorithm. The modified method, which combines the pattern classification capability of the Kohonen network and the optimal choice capacity of OLS, avoids the random selection of RBFNN centers and improves the compensation accuracy of the RBFNN. It can quickly and accurately identify the effect of temperature on laser gyro zero bias. A number of comparable identification and compensation tests on a variety of temperature-changing situations are completed using the multiple linear regression (MLR), RBFNN and modified RBFNN methods. The test results based on several sets of gyro output in constant and changing temperature conditions demonstrate that the proposed method is able to overcome the effect of randomly selected RBFNN centers. The running time of the method is about 60 s shorter than that of traditional RBFNN under the same test conditions, which suggests that the calculations are reduced. Meanwhile, the compensated gyro output accuracy using the modified method is about 7.0 × 10−4 °/h; comparatively, the traditional RBFNN is about 9.0 × 10−4 °/h and the MLR is about 1.4 × 103 °/h. View Full-Text
Keywords: laser gyro; temperature compensation; radial basis function neural network; Kohonen network; orthogonal least squares laser gyro; temperature compensation; radial basis function neural network; Kohonen network; orthogonal least squares

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ding, J.; Zhang, J.; Huang, W.; Chen, S. Laser Gyro Temperature Compensation Using Modified RBFNN. Sensors 2014, 14, 18711-18727.

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