BP NEURAL NETWORK-BASED SMOG ENVIRONMENT AND THE RISK MODEL OF MOOD DRIVING

被引：3

作者：

You, Z. ^{[1
,3
]}

Liu, J. ^{[2
]}

Dai, J. ^{[2
]}

Liu, W. ^{[3
]}

Song, W. ^{[4
]}

Wang, X. ^{[1
]}

Zhang, C. ^{[5
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Digital Media & Design Arts, Beijing, Peoples R China

[2] Beijing Univ Technol, Coll Architecture & Urban Planning, Beijing 100124, Peoples R China

[3] Beijing Normal Univ, Fac Psychol, User Experience Res Ctr, Beijing 100875, Peoples R China

[4] Huaqiao Univ, Coll Mech Engn & Automat, Xiamen 362021, Peoples R China

[5] Wuhan Inst Technol, Sch Elect & Informat Engn, Wuhan 430074, Peoples R China

来源：

APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH | 2017年 / 15卷 / 03期

关键词：

environmental engineering; driving safety; smog; human factor; profile of mood state; REGION; SENSITIVITY; CARBON;

D O I：

10.15666/aeer/1503_17531763

中图分类号：

Q14 [生态学（生物生态学）];

学科分类号：

071012 ; 0713 ;

摘要：

In order to evaluate and study the influence of smog environment on driving safety, this paper utilizes the measurability of mood states, adopts back propagation (BP) artificial neural network instrument to establish a smog-risky mood network model. Six pictures in different smog levels were used as emotional stimuli to verify the BPM neural network-based smog-risky mood relation model. The result shows that the smog is a main factor affecting the driver's mood. The method of estimating moods based on the smog environment of BP network is able to predict 70% of the danger. It was proved to be an effective means for the safety management and self-detection of drivers, especially professional drivers.

引用

页码：1753 / 1763

页数：11

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