山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (1): 63-73.doi: 10.6040/j.issn.1672-3961.0.2022.366
• 机器学习与数据挖掘 • 上一篇
顾玉磊1,马晖2,王愚勤1,胡卉2*,刘富鑫1
GU Yulei1, MA Hui2, WANG Yuqin1, HU Hui2*, LIU Fuxin1
摘要： 为提升航空装备供应商评价标准的适用性及准确性,提出并设计出一种基于逆向传播(back propagation, BP)神经网络-弱分类器(Adaboost)与优劣解距离法(technique for order preference by similarity to an ideal solution, TOPSIS)的航空装备供应商评价方法。运用因子分析理论对供应商评价指标进行筛选,构建适合航空装备行业特点的供应商评价指标体系。在Adaboost算法元框架下,将BP神经网络作为基分类器,设计基于BP-Adaboost强分类器供应商分类模型。针对BP-Adaboost算法无法精确计算供应商综合得分的不足,设计基于TOPSIS法的供应商评价模型。案例分析结果表明,基于BP-Adaboost与TOPSIS法的航空装备供应商评价模型具有更高的评价准确度,对企业完善供应商管理体系提供理论和实践指导。
中图分类号:
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