MOEA/D-DE based bivariate control sequence optimization of a variable-rate fertilizer applicator

•An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was developed.•MOEA/D-DE algorithm was proposed to optimize the control sequence. To realize precise control for a bivariate control system of a variab...

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Veröffentlicht in:	Computers and electronics in agriculture Jg. 167; S. 105063
Hauptverfasser:	Zhang, Jiqin, Liu, Gang, Luo, Chengming, Hu, Hao, Huang, Jiayun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.12.2019 Elsevier BV
Schlagworte:	Algorithms applicators Bivariate analysis Coefficient of variation Differential evolution (DE) Evolutionary algorithms Fertilization fertilizers General Regression Neural Network (GRNN) General regression neural networks Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Multiple objective analysis Optimal control Optimization Precision agriculture Response time Shafts (machine elements) Variable-rate fertilization Precision agriculture General Regression Neural Network (GRNN) Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Variable-rate fertilization Differential evolution (DE)
ISSN:	0168-1699, 1872-7107
Online-Zugang:	Volltext
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