国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

It is an important guarantee for winning the modernization war to upgrade the logistic support capability of airborne pods and to meet the testing requirements of multi-model, multi-fault types and dynamic changes of test environment. Support vector machine (SVM) algorithm is suitable for small samples, high-dimensional, nonlinear classification problems. SVM-related parameters are important factors that affect the performance of the algorithm. The improved SVM algorithm by K-CV and PSO based on the traditional SVM algorithm is used to validate the parameters of the model. The K-CV algorithm is used to cross-validate optimization model parameters .The PSO algorithm is used to dynamically optimize the SVM parameters and a multi-core SVM pod fault diagnosis model is established. Both algorithms can improve the accuracy of the fault diagnosis model, then, improve the learning ability and generalization ability. The optimized SVM fault diagnosis model can effectively quantify and locate the pod fault.