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基于来自多个来源的数据融合预测生产成本的方法,用于工业4.0:机器学习方法的趋势和应用
Masoud Soleimani1, Hossein Naderian2, Amir Hossein Afshinfar3
1Department of Computer Engineering, University of Isfahan, Isfahan, Iran.
Computational intelligence and neuroscience
|October 19, 2023
概括
卷积神经网络 (CNN) 通过整合多源信息,在预测制造成本方面优于人工神经网络 (ANN). CNNs为工业4.0应用提供了更高的准确性和更简单的培训.
科学领域:
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 工业4.0需要先进的制造工艺来提高质量,速度和降低成本.
- 多感官信息融合技术可实现全面的数据采集,改善决策.
- 制造业的预测方法可以是描述性的,预测性的或规范性的.
研究的目的:
- 为了比较人工神经网络 (ANN) 和卷积神经网络 (CNN) 在预测生产成本方面的有效性.
- 将多源信息融合理论应用于神经网络模型的成本预测.
- 在工业4.0.0的背景下评估ANN和CNN的业绩.
主要方法:
- 利用多源信息融合理论来整合来自不同来源的数据.
- 开发并应用ANN和CNN模型用于生产成本预测.
- 在六个成本类别中比较了ANN和CNN的预测准确性.
主要成果:
- 与ANN相比,CNN在预测所有六个成本类别方面表现优越.
- 对于当月的总收入,CNN实现了较低的预测误差 (0.0234).
- 无论是ANN还是CNN模型,都倾向于高估间接成本和直接材料成本.
结论:
- 由于其更高的准确性和更简单的培训,CNN更适合于制造业的多源信息集成.
- 在工业4.0框架内,CNN为预测性成本管理提供了更有效的方法.
- 进一步的研究可能会通过解决对特定成本类型的高估来完善成本预测.
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