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哪位能否帮忙查查文章是否检索,检索号是多少？ 题目: Predication of Sediment Yield Using Wavelet－Neural Networks 作者：Chuan-an Yao, Yong-chang Yu |
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yca(金币+5): 2010-06-17 09:55:00
xiaogounihao(金币+1):谢谢 2010-06-17 15:23:44
yca(金币+5): 2010-06-17 09:55:00
xiaogounihao(金币+1):谢谢 2010-06-17 15:23:44
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Accession number: 20102312991925 Title: Predication of sediment yield using wavelet - Neural networks Authors: Yao, Chuan-An1 ; Yu, Yong-Chang1 Author affiliation: 1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China Corresponding author: Yao, C.-A. (ycagl@163.com) Source title: 2010 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010 Abbreviated source title: Int. Conf. Meas. Technol. Mechatronics Autom., ICMTMA Volume: 2 Monograph title: 2010 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010 Issue date: 2010 Publication year: 2010 Pages: 911-914 Article number: 5459971 Language: English ISBN-13: 9780769539621 Document type: Conference article (CA) Conference name: International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010 Conference date: March 13, 2010 - March 14, 2010 Conference location: Changsha, China Conference code: 80501 Sponsor: Changsha University of Science and Technology; Hunan University of Science and Technology; IEEE Instrumentation and Measurement Society; City University of Hong Kong Publisher: IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States Abstract: Accurate prediction of watershed sediment yield is important for ecological environment and water resources engineering. Based on the advantages of wavelet analysis and neural networks, a new hybrid model of wavelet transform and BP neural network (wavelet-neural network model, WANN) for predicting the sediment yield, has been suggested in this paper. The established WANN model is applied to make quantitative prediction of annual sediment yield for Hepinggou's small watershed, located in the southwest of China's Henan Province. First, the annual sediment yield time series is decomposed and reconstructed into the low-frequency component and the high-frequency components at one-scale level by db2 wavelet, and then both are forecasted respectively with BP neural networks. Finally, the sum of two parts is the predicting result of the annual sediment yield. Results show that the suggested model can improve the forecasting accuracy; it can also be successfully applied to prediction of hydrological time series. © 2010 IEEE. Number of references: 7 Main heading: Neural networks Controlled terms: Forecasting - Landforms - Mechatronics - Sedimentology - Speech recognition - Time series - Water resources - Watersheds - Wavelet analysis - Wavelet transforms Uncontrolled terms: Accurate prediction - BP neural networks - Ecological environments - Forecasting accuracy - Henan Province - High frequency components - Hybrid model - Hydrological time-series - Low-frequency components - Neural network model - Prediction - Quantitative prediction - Sediment yields - Small watersheds Classification code: 723.5 Computer Applications - 731.7 Mechatronix - 751.5 Speech - 922.2 Mathematical Statistics - 912.2 Management - 921 Mathematics - 921.3 Mathematical Transformations - 913.4 Manufacturing - 723.4 Artificial Intelligence - 723.2 Data Processing and Image Processing - 444 Water Resources - 444.1 Surface Water - 461.1 Biomedical Engineering - 481.1 Geology - 608 Mechanical Engineering, General - 703.2.1 Electric Filter Analysis DOI: 10.1109/ICMTMA.2010.794 Database: Compendex Compilation and indexing terms, © 2009 Elsevier Inc. |
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小木虫: 金币+0.5, 给个红包，谢谢回帖
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