Intelligent Global Sensitivity Analysis Based on Deep Learning

doi:10.16183/j.cnki.jsjtu.2021.191

Abstract

Abstract:

This paper proposes an end-to-end method that combines deep learning and sensitivity analysis, which can perform gradient back propagation calculation sensitivity on the saved weight information while training the model. The structure and activation function of the depth model are specially designed to adapt to the subsequent sensitivity calculation. The experimental results conducted on a Boston house prices dataset, a track information fusion dataset, and the G function show that the proposed method is more accurate than classical methods such as Sobol’ method when the parameter distribution is uneven, and has a stronger robustness. Compared with the traditional neural network method, the accuracy of the proposed method is higher. The experiment proves that the sample parameter sensitivity obtained by the deep learning model can be used to optimize the model output.

Key words: convolutional neural network, Inception module, sensitivity analysis, Sobol’ method, nonlinear system analysis, deep learning

CLC Number:

TB112

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.

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模型	测试集 MSE	测试集 MAE	测试集平均误差率/%
NN	0.268	2.21	13.50
CNN	0.095	1.62	9.90
SInception-CNN	0.072	1.54	9.41

参数	SInception-CNN		参数	Sobol’法
参数	正常	加入脏样本	参数	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)

特征	定义
CRIM	城镇人均犯罪率
ZN	占地2322.576平方米以上住宅用地比例
INDUX	每个城镇工业用地的比例
CHAS	查尔斯河(如果临近河流则为1;否则为0)
NOX	一氧化氮浓度
RM	住宅平均房间数
AGE	1940年建造的房屋比例
DIS	与5个就业中心的距离加权和
RAD	公路通达性
TAX	资产税率
PTRATIO	师生比例
B	黑人比例
LSTAT	低收入人口比例