a method for recognizing fatigue driving based on dempster-shafer theory and fuzzy neural network

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ID: 236640
2017
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Abstract
This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving. This method measures driving states using multifeature fusion. First, FNN is introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the combination of revised evidence based on Dempster’s rule. Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the combination of information given by multiple features. The proposed method can also effectively and accurately describe driving states.
Reference Key
zhu2017mathematicala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;WenBo Zhu;Huicheng Yang;Yi Jin;Bingyou Liu
Journal journal of power sources
Year 2017
DOI
10.1155/2017/6191035
URL
http://dx.doi.org/10.1155/2017/6191035
Keywords
civil engineering (general)mathematics engineering (general)

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