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evidence theory
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  evidence theory
Rulegraphs check this compatibility between unary and binary rules by combining evidence theory with graph theory.
      
Formalisms such as probability theory and Dempster-Shafer evidence theory have proposed specific forms for these operators.
      
Both techniques rely on the mathematical tools of evidence theory, which are customized here for application to total uncertainty in track forecasts.
      
Application of evidence theory to quantify uncertainty in hurricane/typhoon track forecasts
      
This essay investigates this connection in detail, pointing to the usefulness of evidence theory to formalise and extend Shackle's decision theory.
      
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This paper generalizes the concept of random sets, which were used by Dempster, to Boolean algebra, and discusses the relationship between the uncertainty structure of information sources and the uncertainty structure of hypothesis spaces. Generalizing the concepts of upper and lower probabilities, the paper gives a kind of interpretation for the generalization of evidence theory which was defined by Guan and Bell, and proves that the conditioning belief functions defined by Guan and Bell is,in fact, a...

This paper generalizes the concept of random sets, which were used by Dempster, to Boolean algebra, and discusses the relationship between the uncertainty structure of information sources and the uncertainty structure of hypothesis spaces. Generalizing the concepts of upper and lower probabilities, the paper gives a kind of interpretation for the generalization of evidence theory which was defined by Guan and Bell, and proves that the conditioning belief functions defined by Guan and Bell is,in fact, a generalization of Dempster's rule of condition. The interpretation method, on the one hand, further develops the generalization of evidence theory, and on the other hand,supplies a feasible application environment for the generalization of evidence theory.

本文把Dempster使用的随机集合概念推广到了布尔代数上,并用这个推广了的概念讨论了信息源上的不确定性结构与假设空间上的不确定性结构之间的关系.利用这个关系,本文推广了上、下概率的概念,并用其解释了Guan和Bell提出的广义证据理论,同时还证明了Guan和Bell定义的条件信任函数,实际上是Dempster条件规则的推广.本文的解释方法一方面拓宽了广义证据理论的内容,另一方面亦为广义证据理论的应用提供了有效的途径.

Evidence theory is a uncertain inference method introduced by Dempster and Shafer,which has important application in expert systems.In this paper,the application of evidence theory to quantitative decision model is considered.The concept of belief degree for quantitative decision model and its calculation approach are proposed.A decision rule integrating optimality and belief degree with policymaker's experiences is presented in complex environment.

证据理论是由Dempster和Shafer于60~70年代提出的一种不确定推理方法,在专家系统中有着重要的应用价值。本文研究了将证据理论用于定量决策模型,提出了定量决策模型的信度概念及其计算方法,并给出了在复杂环境下的最优性与信度及决策人的经验相结合的决策方法。

A method based on the combination of artificial neural networks (ANN) and evidence theory to recognize spatial point targets is brought forward in the paper. The structure of the method is introduced, while the performance of the method is analyzed. The good characteristics for the method to defying disturbance and its robustness are tested and verified by experiments.

介绍了一种基于人工神经网络与证据理论相结合的空间点目标识别以及该方法的结构,分析了该方法的性能,并用计算机仿真验证了该方法的强抗干扰性和鲁棒性

 
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