| 缺失风浪数据补足的神经网络模型 |
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| 引用本文： | 蒋学炼,李炎保.缺失风浪数据补足的神经网络模型[J].海洋科学,2009,33(2):60-67. |
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| 作者姓名： | 蒋学炼 李炎保 |
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| 作者单位： | 1. 天津城市建设学院天津市软土特性与工程环境重点实验室,天津,300384;天津大学建筑工程学院,天津,300072
2. 天津大学建筑工程学院,天津,300072 |
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| 摘 要： | 基于波浪数据的完备性对于海岸海洋工程设计而言非常关键,详细阐述了风浪观测数据补足神经网络模型的建立方法,构建了两个网络模型,以已有观测资料为样本进行了验证.结果表明,两个网络的训练效果均很好,且单输出目标的分层模拟要优于多输出目标的单层模拟.表明了利用人工神经网络推导缺失波浪条件的可行性.
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| 关 键 词： | 波浪观测 缺失 补足 人工神经网络 |
| 收稿时间： | 2006/5/19 0:00:00 |
| 修稿时间： | 2008/9/29 0:00:00 |
An artificial neural network (ANN) model for supplement of deficient wave observation |
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| JIANG Xue-lian and LI Yan-bao.An artificial neural network (ANN) model for supplement of deficient wave observation[J].Marine Sciences,2009,33(2):60-67. |
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| Authors: | JIANG Xue-lian and LI Yan-bao |
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| Institution: | 1.School of Civil Engineering;Tianjin University;Tianjin 300072;China;2.Tianjin Key Laboratory of Soft Soil Characteristics & Engineering Environment;Tianjin Institute of Urban Construction;Tianjin 300384;China |
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| Abstract: | The complete wave observation is very important for the design of coastal and ocean structures.First,the process of establishing a wave prediction model based on artificial neural network(ANN) technique is described in detail.Then,two models are established and verified by employing historical observation data.Comparisons between the results of two models show that the training results of two networks match the samples well and network with single output is better than network with multiple outputs.Owing to... |
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| Keywords: | wave observation deficiency supplement artificial neural network (ANN) |
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