CALIBRATION OF BONDING MODEL PARAMETERS FOR COATED FERTILIZERS BASED ON PSO-BP NEURAL NETWORK

被引：1

作者：

Du, Xin ^{[1
]}

Liu, Cailing ^{[1
]}

Jiang, Meng ^{[1
]}

Yuan, Hao ^{[1
]}

Dai, Lei ^{[1
]}

Li, Fanglin ^{[1
]}

Gao, Zhanpeng ^{[1
]}

机构：

[1] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

来源：

INMATEH-AGRICULTURAL ENGINEERING | 2021年 / 65卷 / 03期

关键词：

Coated fertilizer; Parameter calibration; Proxy Model; PSO-BP Neural Network; DESIGN;

D O I：

10.35633/inmateh-65-27

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

In this paper, the ultimate crushing displacement Y-1 and load Y-2 of the coated fertilizer granules were obtained by uniaxial compression test as 0.450 mm and 58.668 N, respectively. The Plackett-Burman and Steepest ascent tests were taken to determine factors that had significant effects on the results and their ranges of values, respectively. Finally, the Particle Swarm Optimization - Back Propagation (PSO-BP) neural network was trained, and the correlation coefficients of training, validation, testing and overall performance were obtained as 0.98057, 0.95781, 0.96724 and 0.97459, respectively. The Y-1 and Y-2 are 0.450 mm and 58.703N, with a relative error of 0.06% from the actual value.

引用

页码：255 / 264

页数：10

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