开发和性能评估可变宽度的升床前用RBF神经网络-PSO技术预测的最佳参数.
Chetankumar Prakash Sawant1, Bhaskar Bharat Gaikwad2, Ajit Pralhad Magar3
1ICAR-Central Institute of Agricultural Engineering, Bhopal, 462 038, Madhya Pradesh, India. chetankumarsawant@gmail.com.
Scientific reports
|January 12, 2026
概括
这项研究开发了一种可变宽的升床,用于高效的农业. 先进的建模技术优化了它的性能,减少了现场操作中的能源消耗.
科学领域:
- 农业工程 农业工程
- 土壤科学 土壤科学
- 计算机建模 计算建模
背景情况:
- 提高床种植提高了土壤健康,水效率和能源使用.
- 特定作物需求需要可适应的机器,如可变宽度床制造器.
研究的目的:
- 开发和评估一个可变宽度的升床前.
- 使用先进的计算方法优化其操作参数.
主要方法:
- 土壤箱实验分析了不同土壤湿度,工作宽度和前进速度下的起草力.
- 回归和辐射基函数 (RBF) 神经网络模拟了特定的草案.
- RBF神经网络-粒子群优化 (PSO) 技术优化了输入参数.
主要成果:
- 无论是回归模型还是RBF模型都准确地预测了特定的草稿 (R2 > 0.98).
- 最佳参数产生了接近土壤箱测试实际值的具体草稿.
- 现场测试显示,预测和实际具体草案之间的差异为±7.2%.
结论:
- 可变宽度的升床形成器对于各种作物需求是有效的.
- 神经网络和PSO技术成功地优化了耕作机械的参数.
- 这些方法减少了农业现场操作中的能源支出.
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