Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7257
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorTang, QH-
dc.creatorLiu, BH-
dc.creatorChen, YQ-
dc.creatorZhou, XH-
dc.creatorLin, XB-
dc.date.accessioned2015-11-10T08:32:41Z-
dc.date.available2015-11-10T08:32:41Z-
dc.identifier.issn1671-3044-
dc.identifier.urihttp://hdl.handle.net/10397/7257-
dc.language.isozhen_US
dc.publisher海军海洋测绘研究所en_US
dc.rights© 2009 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。en_US
dc.rights© 2009 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research use.en_US
dc.subjectSeabed classificationen_US
dc.subjectBP classificationen_US
dc.subjectGAen_US
dc.subjectMultibeam sounding systemen_US
dc.subjectBackscatter strengthen_US
dc.title基于改进BP神经网络的海底底质分类en_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: 唐秋华en_US
dc.description.otherinformationAuthor name used in this publication: 刘保华en_US
dc.description.otherinformationAuthor name used in this publication: 陈永奇en_US
dc.description.otherinformationAuthor name used in this publication: 周兴华en_US
dc.description.otherinformationTitle in Traditional Chinese: 基于改進BP神經網絡的海底底質分類en_US
dc.description.otherinformationJournal title in Traditional Chinese: 海洋測繪en_US
dc.identifier.spage40-
dc.identifier.epage43-
dc.identifier.volume29-
dc.identifier.issue5-
dcterms.abstract通过采用遗传算法优化神经网络初始权值的方法,将GA算法与BP神经网络有机结合,应用于海底底质分类。基于多波束测深系统获取的反向散射强度数据,应用改进的BP神经网络分类方法,实现对海底基岩、砾石、砂、细砂和泥等底质类型的快速、准确识别。通过实验比较,GA-BP神经网络分类精度明显高于BP神经网络,证明了该方法的有效性和可靠性。-
dcterms.abstractGenetic algorithm(GA) is used to optimize the initial values of BP neural network,and GA-BP neural network is applied in the seabed classification.Using the backscatter data from multibeam echo sounder,the improved BP neural network is used to identify all kinds of seabed sediment types,such as rock,gravel,sand,fine sand and mud on the study area.Comparing the classification precisions between GA-BP and BP,the experiment results indicate that the GA-BP neural network approach is effective and reliable in the acoustic seabed classification.-
dcterms.accessRightsopen accessen_US
dcterms.alternativeSeabed classification with improved BPN neural network-
dcterms.bibliographicCitation海洋测绘 (Hydrographic surveying and charting), Sept. 2009, v. 29, no. 5, p. 40-43, 56-
dcterms.isPartOf海洋测绘 (Hydrographic surveying and charting)-
dcterms.issued2009-
dc.identifier.rosgroupidr46695-
dc.description.ros2009-2010 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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