上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (7): 840-849.doi: 10.16183/j.cnki.jsjtu.2021.191
收稿日期:
2021-07-22
出版日期:
2022-07-28
发布日期:
2022-08-16
通讯作者:
李建勋
E-mail:lijx@sjtu.edu.cn.
作者简介:
吴庶宸(1996-),男,北京市人,硕士生,主要研究方向为参数灵敏度分析.
基金资助:
WU Shuchen1, QI Zongfeng2, LI Jianxun1()
Received:
2021-07-22
Online:
2022-07-28
Published:
2022-08-16
Contact:
LI Jianxun
E-mail:lijx@sjtu.edu.cn.
摘要:
提出一种将深度学习与灵敏度分析结合的端到端方法, 对深度模型的结构和激活函数进行特殊设计以适应后续灵敏度的计算,深度模型训练的同时对保存的权重信息进行反向传播计算灵敏度.在波士顿房价数据集、航迹融合数据集和G函数上的实验结果表明,所提方法相比于Sobol’法等经典方法在参数分布不均匀时准确性更高, 具备更强的鲁棒性, 相比于使用传统神经网络的方法准确性更高.此外,通过实验验证了基于深度学习模型的样本参数灵敏度可以优化模型的输出结果.
中图分类号:
吴庶宸, 戚宗锋, 李建勋. 基于深度学习的智能全局灵敏度分析[J]. 上海交通大学学报, 2022, 56(7): 840-849.
WU Shuchen, QI Zongfeng, LI Jianxun. Intelligent Global Sensitivity Analysis Based on Deep Learning[J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 840-849.
表2
SInception-CNN和Sobol’法鲁棒性对比
参数 | SInception-CNN | 参数 | Sobol’法 | |||
---|---|---|---|---|---|---|
正常 | 加入脏样本 | 正常 | 加入脏样本 | |||
房屋总面积 | 61.52(1) | 64.07(1) | 房屋总面积 | 64.25(1) | 93.64(1) | |
地上居住面积 | 5.22(2) | 5.49(2) | 房屋一层面积 | 10.52(2) | 0.13(8) | |
杂项物品总价值 | 5.05(3) | 4.37(3) | 地下室已装修面积 | 9.51(3) | 0.55(5) | |
地下室已装修面积 | 4.47(4) | 3.96(4) | 杂项物品总价值 | 3.96(4) | 1.15(3) | |
地下室未装修面积 | 4.36(5) | 3.88(5) | 车库面积 | 3.05(5) | 0.12(9) |
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