Interpolation neural network model of a manufactured wind turbine

被引：12
作者：
de Jesus Rubio, Jose [1 ]
机构：
[1] Inst Politecn Nacl, ESIME Azcapotzalco, Secc Estudios Posgrad & Invest, Ave Granjas 682, Mexico City 02250, DF, Mexico
来源：
NEURAL COMPUTING & APPLICATIONS | 2017年 / 28卷 / 08期
关键词：
Neural networks; Interpolation; Hybrid techniques; Wind turbine; Incomplete data; HYBRID; SPARSE; APPROXIMATION; BRAIN;
D O I：
10.1007/s00521-015-2169-4
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, an interpolation neural network is introduced for the learning of a wind turbine behavior with incomplete data. The proposed hybrid method is the combination of an interpolation algorithm and a neural network. The interpolation algorithm is applied to estimate the missing data of all the variables; later, the neural network is employed to learn the output behavior. The proposed method avoids the requirement to know all the system data. Experiments show the effectiveness of the proposed technique.
引用
收藏
页码：2017 / 2028
页数：12
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