Papers by Keyword: Dempster Shafer Evidence Theory

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Authors: Yong Deng, Xiao Yan Su, Wen Jiang
Abstract: Selecting a plant location can be seen as a multiple-criteria decision-making (MCDM) problem. In this paper, a new decision model based on our proposed fuzzy Dempster Shafer method for selecting plant location under linguistic environments is presented. The decision result can be obtained through Dempster combination rule. It is shown that the proposed method can efficiently deal with uncertain information processing in MCDM. A numerical example to select plant location is used to illustrate the efficiency of the proposed method.
831
Authors: Ling Ling Li, Zhi Gang Li, Meng Wu, Chun Tao Zhao
Abstract: Decision-making is necessary in the process of product design, but in many cases, about the information of decision-making, there are various types of uncertainty, for example, ambiguity, incomplete reliability,ignorance and so on. For making the comprehensive treatment to the various types uncertainty information, this paper proposes a method of decision-making based on Dempster-Shafer evidence theory, and uses a new post-processing method to deal with the results. By means of testing and verifying the new method with specific design examples, and proved the method can be effectually applied in the product design.
2724
Authors: Xi Qin Li, Su Yan Cai, Bing Liu, Xue Qun He
Abstract: To reduce emissions, CNG engines are commonly equipped with three-way catalytic converters. However when the engines run at transient conditions, the air fuel ratio can not be precisely controlled at theoretical value by traditional means, so the catalytic converters can not achieve their desired effect. This paper presents a new method for CNG engines to control air fuel ratio at transient conditions. The moments which intake and exhaust valves open are used as the trigger signals for ECU to collect the test data simultaneously. The dynamic information of CNG engine is detected by multiple sensors; the nonlinear coupling relationship between air fuel ratio of CNG engine and the operating conditions are established through information fusion and neural network control. The requirement of real time control for air fuel ratio is achieved, so the emissions of CNG engines are reduced further.
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