{"title":"一种改进神经网络算法在通信噪声信号分类中的应用","authors":"Changren Yu, Lianxing Jia, Yintao Hou","doi":"10.1109/IAEAC.2018.8577927","DOIUrl":null,"url":null,"abstract":"The genetic algorithm is used to improve the neural network and obtain the optimal weights and thresholds. Using the generated optimal weights and thresholds to run the neural network reduces the error norm over neural network which use random weights and thresholds. The classification of three kinds of communications noise signals was simulated by this algorithm, and the accuracy of this algorithm was proved. Experiments show that the algorithm will have an excellent application prospect in the noise signals classification in the communications field.","PeriodicalId":6573,"journal":{"name":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"1 1","pages":"2033-2036"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of an Improved Neural Network Algorithm in Classification of Communication Noise Signals\",\"authors\":\"Changren Yu, Lianxing Jia, Yintao Hou\",\"doi\":\"10.1109/IAEAC.2018.8577927\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The genetic algorithm is used to improve the neural network and obtain the optimal weights and thresholds. Using the generated optimal weights and thresholds to run the neural network reduces the error norm over neural network which use random weights and thresholds. The classification of three kinds of communications noise signals was simulated by this algorithm, and the accuracy of this algorithm was proved. Experiments show that the algorithm will have an excellent application prospect in the noise signals classification in the communications field.\",\"PeriodicalId\":6573,\"journal\":{\"name\":\"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"volume\":\"1 1\",\"pages\":\"2033-2036\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAEAC.2018.8577927\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAEAC.2018.8577927","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of an Improved Neural Network Algorithm in Classification of Communication Noise Signals
The genetic algorithm is used to improve the neural network and obtain the optimal weights and thresholds. Using the generated optimal weights and thresholds to run the neural network reduces the error norm over neural network which use random weights and thresholds. The classification of three kinds of communications noise signals was simulated by this algorithm, and the accuracy of this algorithm was proved. Experiments show that the algorithm will have an excellent application prospect in the noise signals classification in the communications field.