基于BPNN-LSTM的双层车辆速度预测用于越野车辆
Jichao Liu1, Yanyan Liang1, Zheng Chen2
1Jiangsu XCMG Research Institute Co., Ltd., Xuzhou 221004, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种新的双层车辆速度预测 (VSP) 方法,使用反向传播神经网络 (BPNN) 和长期短期记忆 (LSTM) 进行越野车辆的预测,显著提高了预测的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
背景情况:
- 准确的车辆速度预测 (VSP) 对能源管理至关重要,特别是对于现有方法有限的越野车辆.
- 越野车辆动态为VSP带来了独特的挑战,需要专门的方法.
研究的目的:
- 开发和评估一种针对越野车辆量身定制的新型双层VSP方法.
- 为了提高VSP的准确性和实时性能,用于诸如采矿卡车和装载机等重型机械.
主要方法:
- 提出了一个双层框架,集成一个长期短期记忆 (LSTM) 网络用于速度预测和一个反向传播神经网络 (BPNN) 进行信息更新.
- 在制定VSP问题时,考虑了越野车辆的运动特征和变量关系.
- 使用采矿卡车和装载器数据集对分析,BPNN和循环神经网络 (RNN) 方法进行性能评估.
主要成果:
- 与现有方法相比,拟议的BPNN-LSTM方法显示出更高的速度预测准确性.
- 平均预测错误减少了48.14% (与分析),35.82% (与BPNN) 和30.09% (与RNN) 的平均预测错误.
- 该方法保持了实时预测性能,同时提高了准确性.
结论:
- 开发的双层BPNN-LSTM VSP方法为越野车辆应用提供了显著的进步.
- 这种方法为改善越野车辆速度预测准确性和能源管理提供了新的,有效的解决方案.
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