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.

针对短波接收系统难以获取大量标记样本的问题，提出了一种基于改进的拉普拉斯支持向量机算法的短波接收系统智能诊断方法。通过将邻域密度思想引入拉普拉斯支持向量机的邻接图构造中，可以更充分地挖掘样本的流形结构信息，从而提高了拉普拉斯支持向量机分类器的性能，实现了传统拉普拉斯支持向量机的优化。使用KFCM聚类算法选择未标记的边界样本和标记的样本以形成约简训练集。将KFCM预选样本的方法与改进的Laplacian SVM算法相结合，以提高学习效率。