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首页> 外文会议>Conference on Applied Optics and Photonics China >Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network
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Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network

机译：基于K-均值聚类和OLS RBF神经网络的MEMS陀螺仪随机漂移预测

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摘要

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 clusteringOLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007o/s and the prediction time of which is 2.4169e-006s.
机译：传感器的测量误差可以通过预测得到有效补偿。针对MEMS陀螺仪较大的随机漂移误差，提出了一种基于K均值聚类和正交最小二乘（OLS）的径向基函数神经网络的改进学习算法。这篇报告。该算法首先选择典型样本作为RBF NN的初始聚类中心，其次以K-means算法为候选中心，再以OLS算法为候选中心进行优化，简化了网络结构，提高了预测性能。实验结果表明，提出的K-means clusteringOLS学习算法可以有效地预测MEMS陀螺仪的随机漂移，其预测误差为9.8019e-007o / s，预测时间为2.4169e-006s。

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