Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks
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- 作者：Shuangyin Liua ; b ; c ; d ; 1Author Vitae ; Longqin Xua ; 1Author Vitae ; Daoliang Lib ; c ; d ; dliangl@cau.edu.cn" class="auth_mail" title="E-mail the corresponding author ; hdlsyxlq@126.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Empirical mode decomposition ; Back-propagation neural network ; Water temperature ; Multi-scale prediction
- 刊名：Computers & Electrical Engineering
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：49
- 期：Complete
- 页码：1-8
- 全文大小：1709 K
文摘
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The novel model which combines EMD and BPNN algorithm is presented to predict water temperature in intensive aquaculture..
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Using EMD technology adaptively decomposed the original water temperature data into a finite set of IMFs and a residue.
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EMD-BPNN has higher prediction accuracy and better generalization performance than standard BPNN and standard SVR.
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EMD-BPNN can be used as a suitable and effective modeling tool for predicting water temperature in intensive aquaculture.