Calibration study of moisture production parameters model based on neural network by LM algorithm

Author

Ling, Liu ; Yong-zhi, Zhao ; Lei, Wang ; Yang-ren, Wang

Author_Institution

Hydraulic Eng. Dept., Tanjin Agric. Univ., Tianjin, China

fYear

2011

fDate

27-29 May 2011

Firstpage

600

Lastpage

604

Abstract

The is a kind of effective method of water-saving irrigation. Crop moisture production parameters model provide relationship between the output and the evapotranspiration. The article Using improved BP neural network based LM algorithm calibrate Jensen model, and solve moisture sensitivity by test results of winter wheat moisture production parameters in Shanxi Province Xiaohe area. Using this method to solve moisture sensitivity has higher precision and can provide technical guidance to inadequately irrigation.

Keywords

backpropagation; crops; evaporation; irrigation; moisture; neural nets; transpiration; water conservation; BP neural network; Jensen model; LM algorithm; Shanxi Province Xiaohe area; calibration study; crop moisture production parameter model; evapotranspiration; moisture sensitivity; water-saving irrigation; winter wheat moisture production parameters; Data models; Indexes; Irrigation; Mathematical model; Moisture; Production; Jensen model; LM algorithm; moisture sensitivity;

fLanguage

English

Publisher

ieee

Conference_Titel

New Technology of Agricultural Engineering (ICAE), 2011 International Conference on

Conference_Location

Zibo

Print_ISBN

978-1-4244-9574-0

Type

conf

DOI

10.1109/ICAE.2011.5943868

Filename

5943868