As an extension of Dempster–Shafer (D-S) theory, the evidential reasoning (ER) rule can be used as a combination strategy in ensemble learning to deeply mine classifier information through decision-making reasoning. The weight of evidence is an important parameter in the ER rule, which has a significant effect on the result of ensemble learning. However, current research results on the weight of evidence are not ideal, leveraging expert knowledge to assign weights leads to the excessive subjectivity, and using sample statistical methods to assign weights relies too heavily on the samples, so the determined weights sometimes differ greatly from the actual importance of the attributes. Therefore, to solve the problem of excessive subjectivity and objectivity of the weights of evidence, and further improve the accuracy of ensemble learning based on the ER rule, we propose a novel combination weighting method to determine the weight of evidence. The combined weights are calculated by leveraging our proposed method to combine subjective and objective weights of evidence. The regularization of these weights is studied. Then, the evidential reasoning rule is used to integrate different classifiers. Five case studies of image classification datasets have been conducted to demonstrate the effectiveness of the combination weighting method.
Lithium-ion batteries are widely used in energy storage, small electronic devices and other fields due to their advantages of high energy density and long life cycles, as well as causing less damage to the environment than alternatives. For safety, it is essential to propose reasonable methods to assess batteries’ health statuses. Therefore, a health assessment model based on the evidential reasoning (ER) rule is proposed in this article. Firstly, the voltage rise time and the current fall time are taken as observation indicators, which contain information about the health status of lithium-ion batteries. Secondly, the information of various indicators is integrated into a belief structure, and the indicator reliability and indicator weights are adequately considered in the assessment model. Thirdly, there are some perturbations that will affect the operating status of batteries and cause the batteries’ reliability to fluctuate, so we use perturbation analysis to determine the adaptability of batteries to perturbations. We set two bounded parameters, the perturbation coefficient and the maximum perturbation error, to assess the reliability of lithium-ion batteries when experiencing perturbations. Finally, on the basis of the whole-life open data set of lithium-ion batteries from the National Aeronautics and Space Administration’s Prognostics Center of Excellence, the validity of the health assessment model and perturbation analysis is demonstrated.
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