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dc.contributor.author刘丽桑
dc.contributor.author彭侠夫
dc.date.accessioned2016-05-17T02:43:35Z
dc.date.available2016-05-17T02:43:35Z
dc.date.issued2011
dc.identifier.citation船舶力学,2011,(5):22-26
dc.identifier.issn1007-7294
dc.identifier.otherCBLX201105008
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/105346
dc.description.abstract为了提高船舶的耐波性和适航性、对船舶横摇进行有效准确预报,提出了将灰色系统理论和神经网络进行有机结合的二阶灰色神经网络预报模型。介绍了二阶灰色预报模型,采用神经网络映射的办法构建灰色神经网络预报模型,并介绍了神经网络学习机制。另外,以某舰船横摇运动时间序列预报为例对模型进行仿真验证,有效改善了二阶灰色模型较大的预报偏差。仿真结果表明,gnnM(2,1)模型能准确预报船舶横摇运动,具有更高的预报精度和更好的数据稳定性。
dc.description.abstractTo enhance the ship’s seakeeping capacity and seaworthiness, a second order Gray Neural Network forecasting model is presented to forecast roll motion accurately.The gray system and its gray model are introduced, then using neural network mapping approach to build the second order GNNM(2, 1) model.On the other hand, the learning algorithm is presented.Further more, GNNM(2, 1) is applied in a sample of ship roll series and effectively improves large prediction error of second order gray model.The simulation results prove that the new model is more accurate and stable than tradition models.
dc.description.sponsorship985工程学科建设项目(0000-x07204)
dc.language.isozh_CN
dc.subject灰色神经网络
dc.subject船舶横摇
dc.subject预报
dc.subjectGNNM(2
dc.subject1)
dc.subjectGray Neural Network
dc.subjectship roll
dc.subjectforecast
dc.subjectGNNM(2
dc.subject1)
dc.title二阶灰色神经网络在船舶横摇预报中的应用
dc.title.alternativeSecond order Gray Neural Network in ship roll forecast
dc.typeArticle


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