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A study on intelligent diagnosis model of shortwave receiving system based on improved KFCM and LapSVM

机译:基于改进的KFCM和LAPSVM的短波接收系统智能诊断模型研究

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摘要

Aiming at the difficulty of obtaining a large number of labeled samples of the shortwave receiving system, an intelligent diagnosis method for the shortwave receiving system based on the improved Laplacian SVM algorithm is proposed. By introducing the idea of neighborhood density into the adjacency graph construction of Laplacian SVM, the manifold structure information of samples is more fully mined, thus improving the performance of Laplacian SVM classifier and realizing the optimization of traditional Laplacian SVM. KFCM clustering algorithm was used to select unlabeled boundary samples and labeled samples to form the reduction training set. The method of the KFCM pre-selection sample was combined with the improved Laplacian SVM algorithm to enhance the learning efficiency. The simulation results using the UCI data set and the experimental verification results of shortwave receiving system sample data indicate that the proposed algorithm could more fully mine the manifold structure information of samples and improve the performance of the Laplacian SVM classifier.
机译:针对基于改进的LAPLACIAN SVM算法的基于改进的LAPLACIAN SVM算法,提出了一种难以获得短波接收系统的大量标记样本的难度诊断方法。通过将邻域密度的思想引入Laplacian SVM的邻接图构造,样品的歧管结构信息更加彻底,从而提高了Laplacian SVM分类器的性能并实现了传统拉普拉斯SVM的优化。 KFCM聚类算法用于选择未标记的边界样本和标记的样本以形成减少训练集。 KFCM预选择样品的方法与改进的拉普拉斯SVM算法组合,以提高学习效率。使用UCI数据集的仿真结果和短波接收系统样本数据的实验验证结果表明所提出的算法可以更充分地挖掘样本的歧管结构信息并提高拉普拉斯SVM分类器的性能。

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