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Proceedings Paper

Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network
Author(s): Zhen-yu Wang; Li-jie Zhang
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Paper Abstract

Measure error of the sensor can be effectively compensated with prediction. Aiming at large random drift error of MEMS(Micro Electro Mechanical System))gyroscope, an improved learning algorithm of Radial Basis Function(RBF) Neural Network(NN) based on K-means clustering and Orthogonal Least-Squares (OLS) is proposed in this paper. The algorithm selects the typical samples as the initial cluster centers of RBF NN firstly, candidates centers with K-means algorithm secondly, and optimizes the candidate centers with OLS algorithm thirdly, which makes the network structure simpler and makes the prediction performance better. Experimental results show that the proposed K-means clustering OLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007°/s and the prediction time of which is 2.4169e-006s

Paper Details

Date Published:
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Proc. SPIE 10463, AOPC 2017: Space Optics and Earth Imaging and Space Navigation, ; doi: 10.1117/12.2282531
Show Author Affiliations
Zhen-yu Wang, Inner Mongolia Univ. of Technology (China)
Li-jie Zhang, Inner Mongolia Univ. of Technology (China)


Published in SPIE Proceedings Vol. 10463:
AOPC 2017: Space Optics and Earth Imaging and Space Navigation
Carl Nardell; Suijian Xue; Huaidong Yang, Editor(s)

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