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使用改进的粒子群优化算法预测气体生产装置预分裂中的峰值粒子速度
Yawen Cao1,2, Rui Ma3, Keji Zhao4
1North China Engineering Investigation Institute Co.Ltd, Shijiazhuang, 050021, China. 760152262@qq.com.
Scientific reports
|April 21, 2025
概括
一个改进的粒子群集优化 (IPSO) 逆向传播 (BP) 神经网络模型准确地预测了喷射构造的峰值粒子速度 (PPV). 这种先进的模型通过为道和建筑物提供可靠的振动速度预测来提高安全性.
科学领域:
- 地质技术工程 地质技术工程
- 计算智能是一种计算智能.
- 土木工程 土木工程是指土木工程.
背景情况:
- 爆炸工程需要精确的振动速度预测来保护基础设施.
- 现有的模型往往缺乏有效控制振动所需的精度.
研究的目的:
- 开发一个高度准确的预测工具,用于爆破工程中的峰值粒子速度 (PPV).
- 引入一个改进的粒子集群优化 (IPSO) 逆向传播 (BP) 神经网络模型 (IPSO-BP),结合频率效应.
主要方法:
- IPSO-BP模型是使用爆破施工数据开发和训练的.
- 性能与传统的BP神经网络,经验公式和其他高级算法 (如GA-APSO-BP,GWO-SVR,MFO-BP和RUN-XGBoost) 相比进行了评估.
- 对比分析包括训练组的性能,可测试性和整体预测准确性.
主要成果:
- 与所有基准模型相比,IPSO-BP模型显示出优越的预测性能.
- 该模型在训练和测试数据集中显示出高准确度和可靠性.
- 频率 (f) 已成功集成,提高了预测能力.
结论:
- IPSO-BP模型提供了一种可靠和准确的解决方案,用于预测喷气工程中的PPV.
- 这一进步可以显著改善道和周围结构的安全协议.
- 该研究验证了将IPSO与BP神经网络集成到复杂工程预测中的有效性.
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