文献
J-GLOBAL ID:202002285161651486
整理番号:20A1107674
限られた気候データからの日基準蒸発散量予測のための極端学習機械によるハイブリッド粒子群最適化【JST・京大機械翻訳】
Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data
著者 (7件):
Zhu Bin
(State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu, China)
,
Feng Yu
(State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu, China)
,
Feng Yu
(Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China)
,
Gong Daozhi
(Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China)
,
Jiang Shouzheng
(State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu, China)
,
Zhao Lu
(State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu, China)
,
Cui Ningbo
(State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu, China)
資料名:
Computers and Electronics in Agriculture
(Computers and Electronics in Agriculture)
巻:
173
ページ:
Null
発行年:
2020年
JST資料番号:
T0337A
ISSN:
0168-1699
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)