{"title":"基于遗传算法的支持向量机抑制光纤非线性","authors":"Junfeng Zhang, Wei Chen, M. Gao, G. Shen","doi":"10.1109/CLEOPR.2017.8118715","DOIUrl":null,"url":null,"abstract":"We applied genetic algorithm to optimize the parameters of support vector machine for improving prediction accuracy. The proposed method is measured experimentally in 16-QAM coherent communication system for mitigating the fiber-nonlinearity-induced impairments.","PeriodicalId":6655,"journal":{"name":"2017 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR)","volume":"3 1","pages":"1-3"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Mitigating fiber nonlinearity using support vector machine with genetic algorithm\",\"authors\":\"Junfeng Zhang, Wei Chen, M. Gao, G. Shen\",\"doi\":\"10.1109/CLEOPR.2017.8118715\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We applied genetic algorithm to optimize the parameters of support vector machine for improving prediction accuracy. The proposed method is measured experimentally in 16-QAM coherent communication system for mitigating the fiber-nonlinearity-induced impairments.\",\"PeriodicalId\":6655,\"journal\":{\"name\":\"2017 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR)\",\"volume\":\"3 1\",\"pages\":\"1-3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CLEOPR.2017.8118715\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CLEOPR.2017.8118715","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mitigating fiber nonlinearity using support vector machine with genetic algorithm
We applied genetic algorithm to optimize the parameters of support vector machine for improving prediction accuracy. The proposed method is measured experimentally in 16-QAM coherent communication system for mitigating the fiber-nonlinearity-induced impairments.
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