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基于BP神经网络的模型铲刀推土阻力预测模型
  • 期刊名称：农机化研究
  • 时间：0
  • 页码：121-124
  • 语言：中文
  • 分类：TP183[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程] S126[农业科学—农业基础科学]
  • 作者机构：[1]长春大学计算机科学技术学院,长春130022, [2]吉林大学地面机械仿生技术教育部重点实验室,长春130025
  • 相关基金：国家自然科学基金重点资助项目（50635030）;教育部科学技术研究重点项目（107035）;吉林省科技发展计划项目（20050539）
  • 相关项目：微重力环境月壤力学特性模拟分析与评估方法研究
作者： 彭剑波|李建桥|黄葵|李因武|
关键词： BP神经网络, 模型铲刀, 推土阻力, 预测, BP neural network, model blade, draft resistance, prediction
中文摘要：

将BP神经网络应用于典型的曲面推土板-模型铲刀推土阻力的预测。在模型铲刀推土试验研究结果的基础上，以模型铲刀的切削角、前翻角和切削速度为输入，以模型铲刀推土阻力的水平分力与垂直分力为输出，建立了BP神经网络数值模拟模型。研究结果表明，该BP神经网络有效地预测了模型铲刀的推土阻力，其准确率在94％以上。

英文摘要：

The surfaces of soil - engaging components is the combination of curve blade and plane blade commonly, considering the different parameters and conditions involved in the soil - tool interaction system, it became a difficult work to study the interaction between soil and curve blade variously, and it was essential to use numerical simulation instead of physical test to solve this problem. The BP neural network was used to ＂learn＂ results of the numerical simulation to the draft resistance of model blade, it had a structure of 3 ×10 × 2, input facts of the BP neural networks were cutting angle, front reversal angle and forward speed, output facts were horizontal and vertical components of draft resistance. The correctness of the output facts is more than 94% ; it means the performance equation of the adhesion interface system has a high forecasting accuracy and favorable generalization ability.

同期刊论文项目
机械仿生耦合设计原理与关键技术
期刊论文 174 获奖 5 著作 1
微重力环境月壤力学特性模拟分析与评估方法研究
期刊论文 20 会议论文 6 专利 1
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