Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2022 , Vol. 56 >Issue 7: 840 - 849

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.191

Electronic Information and Electrical Engineering

Intelligent Global Sensitivity Analysis Based on Deep Learning

Expand
  • 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 date: 2021-07-22

  Online published: 2022-08-16

Fold

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

Cite this article

WU Shuchen, QI Zongfeng, LI Jianxun . Intelligent Global Sensitivity Analysis Based on Deep Learning[J]. Journal of Shanghai Jiaotong University, 2022 , 56(7) : 840 -849 . DOI: 10.16183/j.cnki.jsjtu.2021.191

References

[1] SALTELLI A, ALEKSANKINA K, BECKER W, et al. Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices[J]. Environmental Modelling and Software, 2019, 114: 29-39.
[2] ZHANG X Y, TRAME M, LESKO L, et al. Sobol sensitivity analysis: A tool to guide the development and evaluation of systems pharmacology models[J]. CPT: Pharmacometrics and Systems Pharmacology, 2015, 4(2): 69-79.
[3] 周云峰, 周永潮, 郑春华, 等. 采用Sobol方法的暴雨径流管理模型参数灵敏度分析[J]. 浙江大学学报(工学版), 2019, 53(2): 347-354.
[3] ZHOU Yunfeng, ZHOU Yongchao, ZHENG Chunhua, et al. Sensitivity analysis of parameters of storm water management model with Sobol method[J]. Journal of Zhejiang University (Engineering Science), 2019, 53(2): 347-354.
[4] 王丰华, 王劭菁, 苏磊, 等. 采用Sobol’算法分析变电站接地网关键参数的灵敏度[J]. 高电压技术, 2017, 43(1): 300-306.
[4] WANG Fenghua, WANG Shaojing, SU Lei, et al. Sensitivity analysis of substation grounding grid parameters based on Sobol’ method[J]. High Voltage Engineering, 2017, 43(1): 300-306.
[5] 何琨, 严正, 徐潇源, 等. 基于Sobol’法的孤岛微电网潮流全局灵敏度分析[J]. 电力系统自动化, 2018, 42(14): 99-106.
[5] HE Kun, YAN Zheng, XU Xiaoyuan, et al. Sobol’ method based global sensitivity analysis of power flow in islanded microgrid[J]. Automation of Electric Power Systems, 2018, 42(14): 99-106.
[6] TANG Y, REED P, WAGENER T, et al. Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation[J]. Hydrology and Earth System Sciences, 2007, 11(2): 793-817.
[7] ROSOLEM R, GUPTA H V, SHUTTLEWORTH W J, et al. A fully multiple-criteria implementation of the Sobol’ method for parameter sensitivity analysis[J]. Journal of Geophysical Research: Atmospheres, 2012, 117: 1-18.
[8] KHORASHADI ZADEH F, NOSSENT J, SARRAZIN F, et al. Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model[J]. Environmental Modelling and Software, 2017, 91: 210-222.
[9] CUKIER R I, LEVINE H B, SHULER K E. Nonlinear sensitivity analysis of multiparameter model systems[J]. Journal of Computational Physics, 1978, 26(1): 1-42.
[10] TARANTOLA S, GATELLI D, MARA T A. Random balance designs for the estimation of first order global sensitivity indices[J]. Reliability Engineering and System Safety, 2006, 91(6): 717-727.
[11] TISSOT J Y, PRIEUR C. Bias correction for the estimation of sensitivity indices based on random balance designs[J]. Reliability Engineering and System Safety, 2012, 107: 205-213.
[12] NOURANI V, SAYYAH FARD M. Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes[J]. Advances in Engineering Software, 2012, 47(1): 127-146.
[13] JAIN S K, NAYAK P C, SUDHEER K P. Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation[J]. Hydrological Processes, 2008, 22(13): 2225-2234.
[14] SONG S F, WANG L. Modified GMDH-NN algorithm and its application for global sensitivity analysis[J]. Journal of Computational Physics, 2017, 348: 534-548.
[15] PARK M Y, HASTIE T. L1-regularization path algorithm for generalized linear models[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2007, 69(4): 659-677.
[16] NOSSENT J, ELSEN P, BAUWENS W. Sobol’ sensitivity analysis of a complex environmental model[J]. Environmental Modelling and Software, 2011, 26(12): 1515-1525.
[17] HADJI I, WILDES R P. What do we understand about convolutional networks?[EB/OL].(2018-05-23)[2021-06-11]. https://arxiv.org/abs/1803.08834.
[18] LIN M, CHEN Q, YAN S. Network in network[EB/OL]. (2013-12-16) [2021-06-11]. https://arxiv.org/abs/1312.4400.
[19] BA, JIMMY LEI, JAMIE RYAN KIROS. Layer normalization[EB/OL] (2016-07-21) [2021-06-13]. https://arxiv.org/abs/1607.06450.
[20] CONFALONIERI R, BELLOCCHI G, BREGAGLIO S, et al. Comparison of sensitivity analysis techniques: A case study with the rice model WARM[J]. Ecological Modelling, 2010, 221(16): 1897-1906.
[21] BHATTACHARJEE K, PANT M, ZHANG Y D, et al. Multiple Instance Learning with Genetic Pooling for medical data analysis[J]. Pattern Recognition Letters, 2020, 133: 247-255.
[22] GRAHAM B. Fractional max-pooling[EB/OL].(2014-12-18) [2021-06-13]. https://arxiv.org/abs/1412.6071.
[23] MARREL A, IOOSS B, LAURENT B, et al. Calculations of Sobol indices for the Gaussian process metamodel[J]. Reliability Engineering and System Safety, 2009, 94(3), 742-751.
[24] ARCHER G E B, SALTELLI A, SOBOL I M. Sensitivity measures, anova-like techniques and the use of bootstrap[J]. Journal of Statistical Computation and Simulation, 1997, 58(2): 99-120.
Options
Outlines

/

〈 〉