| 机车控制电源故障特征向量的维数约简方法研究 |
投稿时间：2018-04-09 修订日期：2018-04-12  点此下载全文 |
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| 基金项目:高速列车受电弓主动控制 |
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| 中文摘要:通过对电力机车控制系统中控制电源的电路结构特点分析,进行故障特征值提取,对所造成的高维数据无法进行模式识别这一特点。通过对传统的流形学习算法中LE理论进行改进,提出了基于马氏距离的LE算法理论,对LE算法中的邻域选择问题进行了深入的研究,使k具有自适应性,而且利用关联维数理论克服了非线性电路的故障特征提取中所造成的维数灾难,对其高维数据进行本征维数的估计,去除了不相关的信息维数,解决了流形学习理论中d的选取难点。最后通过验证得出该方法的有效性与准确性。 |
| 中文关键词:移相全桥变换器 流形学习 数据降维 马氏距离 关联维数 |
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| Study on Dimensionality Reduction Method of Fault Eigenvector of Locomotive Control Power Supply |
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| Abstract:Through the analysis of the circuit structure characteristics of the control power supply in the electric locomotive control system, and extract the feature value of the fault. The pattern recognition of the high-dimensional data can not be carried out. By improving the LE theory in the traditional manifold learning algorithm, we propose a LE algorithm based on Mahalanobis distance. The neighborhood selection problem in LE algorithm is deeply studied, which makes K adaptive. Using correlation dimension theory, overcome the curse of dimensionality caused by fault feature extraction in nonlinear circuits, and the estimation of intrinsic dimension of high-dimensional data removes unrelated information dimension, and solves the difficulty of D selection in manifold learning theory. Finally, the validity and accuracy of the method is obtained through verification. |
| keywords:Phase-shifted full-bridge converter, Manifold learning, Dimensionality reduction, Mahalanobis distance, Correlation dimension |
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