|Table of Contents|

Intelligent Subjective Evaluation of Virtual Road Load Data Accuracy Based on Random Forest(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年04期
Page:
45-54
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Intelligent Subjective Evaluation of Virtual Road Load Data Accuracy Based on Random Forest
Author(s):
Qiu Yu1; Li Wenkui1; Li Mian2; 3
(1.Test & Validation Department,SAIC Motor Technical Center,Shanghai 201804,China)
(2.UM-SJTU Joint Institute,Shanghai Jiao Tong University,Shanghai 200240,China)
(3.School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
Keywords:
road load data; machine learning; random forest; time series signals; subjective evaluation
PACS:
U467.4+97
DOI:
10.3969/j.issn.1672-1292.2022.04.006
Abstract:
Considering the limitations of current methods for virtual road load data evaluation,an intelligent subjective evaluation model of virtual road load data signals based on random forest is proposed. The objective statistical parameters of time history signals are taken as the inputs and the scores of subjective evaluations from experts as the output. The most important six objective statistical parameters are selected through feature importance ranking,which takes various features of the time history signals into a comprehensive consideration. The professional evaluation experiences from many experts are solidified into the model by training,and the fluctuations caused by human factors is avoided. The intelligent subjective evaluation model has high accuracy and good generalization,and it is proven a comprehensive,efficient and accurate intelligent tool for virtual time history signal evaluation for real-world applications.

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Last Update: 2022-12-15