Modeling Vegetation Attenuation Patterns: A Comparison between Polynomial Regressions and Artificial Neural Networks

被引：0

作者：

Gomez-Perez, Paula ^{[1
]}

Crego-Garcia, Marcos ^{[2
]}

Cuinas, Inigo ^{[2
]}

机构：

[1] Ctr Univ Def, Spanish Naval Acad, Marin, Pontevedra, Spain

[2] Univ Vigo, Dept Teoria Sinal & Comunicac, Vigo, Pontevedra, Spain

来源：

2016 IEEE ANTENNAS AND PROPAGATION SOCIETY INTERNATIONAL SYMPOSIUM | 2016年

关键词：

Artificial neural networks; pattern extraction; polynomial regression; vegetation;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Polynomial regressions have been widely used for modeling vegetation patterns. However, artificial neural networks provide more efficient, accurate and generalizable models than polynomial regressions. This paper compares both machine-learning techniques in terms of RMS error and training set size in order to demonstrate the superiority of neural networks over well-known methods as polynomial regressions.

引用

页码：2061 / 2062

页数：2

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