Detalhes bibliográficos
| Autor(a) principal: |
Dong,Zhigui |
| Data de Publicação: |
2022 |
| Outros Autores: |
Song,Qingfeng,
Zhang,Wei |
| Tipo de documento: |
Artigo
|
| Idioma: |
eng |
| Título da fonte: |
Engenharia Agrícola |
| Texto Completo: |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000400204
|
Resumo: |
ABSTRACT To solve the poor fitting degree of errors in multiobjective parameter optimization and low accuracy, a multiobjective optimization method based on a BP neural network was proposed. By taking the 1ZT-210 type whole-straw returning device as the research object, a BP neural network model on power consumption, straw returning rate and the influencing factors was obtained. By optimizing the model by the proposed method, the optimal parameter combination of the test factors was as follows: the advancing speed of the device was 0.65 km/h, the blade roll rotating speed was 210 rpm, the blade installation angle was 55o, the minimum power consumption was 9.82 kW and the maximum straw returning rate was 93.23%. Under such test conditions, the minimum power consumption was 10.75 kW, and the straw returning rate was 92.46%, which were all better than those obtained by the regression analysis method. Finally, a verification test was conducted on the results of BP neural network optimization. The power consumption of the test was 10.04 kW, the absolute error was 0.22 kW and the relative error was 2.24%. For a straw returning rate of 93.11%, the absolute error was -0.12% and the relative error was 0.13%. The test results indicated that the optimization method was feasible. |