Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (7): 840-849.doi: 10.16183/j.cnki.jsjtu.2021.191

• Electronic Information and Electrical Engineering • Previous Articles     Next Articles

Intelligent Global Sensitivity Analysis Based on Deep Learning

WU Shuchen1, QI Zongfeng2, LI Jianxun1()   

  1. 1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronic Information System, Luoyang 471003, Henan, China
  • Received:2021-07-22 Online:2022-07-28 Published:2022-08-16
  • Contact: LI Jianxun


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: