MEEMD Decomposition-Prediction-Reconstruction Model of Precipitation Time Series.

Wang, Yongtao; Liu, Jian; Li, Rong; Suo, Xinyu; Lu, Enhui

Wang, Yongtao; Liu, Jian; Li, Rong; Suo, Xinyu; Lu, Enhui.

Afiliación

Wang Y; State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China.
Liu J; Guizhou Institute of Water Resources Science, Guiyang 550002, China.
Li R; State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China.
Suo X; State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China.
Lu E; State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China.

Sensors (Basel) ; 22(17)2022 Aug 25.

Article en En | MEDLINE | ID: mdl-36080874

Asunto(s)

Redes Neurales de la Computación; Máquina de Vectores de Soporte; Ríos; Factores de Tiempo

Palabras clave

convolutional neural network (CNN); improved overall mean empirical modality (MEEMD); improved overall mean empirical modality decompositionpredictionreconstruction model (MDPRM); particle swarm optimization support vector machine (PSO-SVM); recurrent neural network (RNN)

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Máquina de Vectores de Soporte Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

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