An automatic hyperparameter optimization DNN model for precipitation prediction

Deep neural networks (DNN) have gained remarkable success on many rainfall predictions tasks in recent years. However, the performance of DNN highly relies upon the hyperparameter setting. In order to design DNNs with the best performance, extensive expertise in both the DNN and the problem domain u...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 52; H. 3; S. 2703 - 2719
Hauptverfasser:	Peng, Yuzhong, Gong, Daoqing, Deng, Chuyan, Li, Hongya, Cai, Hongguo, Zhang, Hao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.02.2022 Springer Nature B.V
Schlagworte:	Artificial Intelligence Artificial neural networks Back propagation Back propagation networks Computer Science Gene expression Genetic algorithms Machines Manufacturing Mechanical Engineering Optimization Precipitation Processes Rainfall Searching Support vector machines Neural structure optimization Hyperparameter optimization Deep neural networks Neural architecture search Precipitation prediction Gene expression programming
ISSN:	0924-669X, 1573-7497
Online-Zugang:	Volltext
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