{"title":"基于支持向量机的路基病害识别算法研究","authors":"Huasheng Zou","doi":"10.1109/IASP.2009.5054599","DOIUrl":null,"url":null,"abstract":"Based on the principle of support vector machines as well as the analysis to the characteristics of roadbed diseases, a new GPR echo signal recognition algorithm is brought up. By using the proposed method to identify the ground-penetrating radar measured data, the test results show that this algorithm is better than the neural network recognition algorithm and overcomes the shortcomings of the local minimum value and over-learning. The algorithm is suitable for ground-penetrating radar signal recognition and is an efficient algorithm.","PeriodicalId":143959,"journal":{"name":"2009 International Conference on Image Analysis and Signal Processing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Study on roadbed disease recognition algorithm based on support vector machine\",\"authors\":\"Huasheng Zou\",\"doi\":\"10.1109/IASP.2009.5054599\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the principle of support vector machines as well as the analysis to the characteristics of roadbed diseases, a new GPR echo signal recognition algorithm is brought up. By using the proposed method to identify the ground-penetrating radar measured data, the test results show that this algorithm is better than the neural network recognition algorithm and overcomes the shortcomings of the local minimum value and over-learning. The algorithm is suitable for ground-penetrating radar signal recognition and is an efficient algorithm.\",\"PeriodicalId\":143959,\"journal\":{\"name\":\"2009 International Conference on Image Analysis and Signal Processing\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Image Analysis and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IASP.2009.5054599\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Image Analysis and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IASP.2009.5054599","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on roadbed disease recognition algorithm based on support vector machine
Based on the principle of support vector machines as well as the analysis to the characteristics of roadbed diseases, a new GPR echo signal recognition algorithm is brought up. By using the proposed method to identify the ground-penetrating radar measured data, the test results show that this algorithm is better than the neural network recognition algorithm and overcomes the shortcomings of the local minimum value and over-learning. The algorithm is suitable for ground-penetrating radar signal recognition and is an efficient algorithm.