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ID:37070139
大小:2.68 MB
页数:77页
时间:2019-05-17
《基于卷积神经网络的SAR图像目标识别》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、分类号TP957学号14040075UDC密级公开工学硕士学位论文基于卷积神经网络的SAR图像目标识别硕士生姓名田壮壮学科专业信息与通信工程研究方向雷达信息处理与目标识别技术指导教师张军研究员国防科学技术大学研究生院二〇一六年十一月SARImagesRecognitionBasedonConvolutionalNeuralNetworkCandidate:TianZhuangzhuangAdvisor:ZhangJunAdissertationSubmittedinpartialfulfillmentoftherequirementsforthede
2、greeofMasterofEngineeringinInformationandCommunicationEngineeringGraduateSchoolofNationalUniversityofDefenseTechnologyChangsha,Hunan,P.R.China(November,2016)国防科学技术大学研究生院硕士学位论文目录摘要...................................................................................................
3、..............iABSTRACT........................................................................................................ii第一章绪论......................................................................................................11.1研究背景及意义...............................
4、..............................................................11.2国内外研究现状与发展趋势.........................................................................31.2.1SAR图像目标识别技术发展现状........................................................31.2.2卷积神经网络发展及应用.................................
5、...................................41.3本文主要工作及内容安排.............................................................................4第二章卷积神经网络基本理论.........................................................................72.1引言..........................................................
6、.......................................................72.2神经网络基本理论.........................................................................................72.2.1神经元建模............................................................................................72.2.2网络结构的搭建......
7、............................................................................102.2.3数据预处理及权值初始化..................................................................122.3卷积神经网络理论.......................................................................................132.3.1卷积层........
8、......................................................
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