{"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}
引用次数: 3
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.
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