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.
In reliability analysis of a target, information from various aspects should be analyzed comprehensively. In this process, information fusion plays a key role in integrating and analyzing information from different channels. Aiming at the reliability analysis of discrete variables and continuous variables existing simultaneously, an information fusion method based on Dempster’s rule of combination of D-S evidence theory was proposed to facilitate the reliability analysis by integrating information. Firstly, the continuous variables are discrete by using the continuous variables discretization algorithm based on the probability distribution of the job state with equal expectation scale, and the basic probability distribution of each discrete variable is carried out. Then Dempster’s synthesis rule was used to synthesize the discrete variables. Deng entropy was used as the judgment standard in the synthesis process, so that the entropy of each step of fusion was reduced to ensure the credibility of fusion was increased. Finally, the feasibility and effectiveness of the method are verified by a case.
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