scholarly journals A Method to Determine Generalized Basic Probability Assignment in the Open World

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Wen Jiang ◽  
Jun Zhan ◽  
Deyun Zhou ◽  
Xin Li
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Triangular Fuzzy Number ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Open World ◽  
Differentiation Degree ◽  
Basic Probability ◽  
Open Issue ◽  
The Given

Dempster-Shafer evidence theory (D-S theory) has been widely used in many information fusion systems since it was proposed by Dempster and extended by Shafer. However, how to determine the basic probability assignment (BPA), which is the main and first step in D-S theory, is still an open issue, especially when the given environment is in an open world, which means the frame of discernment is incomplete. In this paper, a method to determine generalized basic probability assignment in an open world is proposed. Frame of discernment in an open world is established first, and then the triangular fuzzy number models to identify target in the proposed frame of discernment are established. Pessimistic strategy based on the differentiation degree between model and sample is defined to yield the BPAs for known targets. If the sum of all the BPAs of known targets is over one, then they will be normalized and the BPA of unknown target is assigned to0; otherwise the BPA of unknown target is equal to1minus the sum of all the known targets BPAs. IRIS classification examples illustrated the effectiveness of the proposed method.

Determination of Generalized Basic Probability Assignment Based on Interval Numbers

2014 ◽  
Vol 644-650 ◽  
pp. 934-938
Author(s):  
Rui Hong Wang ◽  
Pei Da Xu ◽  
Xin Chen ◽  
Yong Deng
Keyword(s):  
Fuzzy Theory ◽  
Evidence Theory ◽  
Interval Number ◽  
Interval Numbers ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Open World ◽  
Basic Probability

On the basis of the determination of basic probability assignment based on interval numbers, and combine the generalized evidence theory in the open world, the paper proposed an approach to determine generalized basic probability assignment based on the interval number, which offered a new idea of the determination of generalized basic probability besides the determination based on fuzzy theory. The rationality and effectiveness are verified by the experiments.

scholarly journals Visualization of basic probability assignment

2021 ◽  
Author(s):  
Hong Feng Long ◽  
Zhen Ming Peng ◽  
Yong Deng
Keyword(s):  
Evidence Theory ◽  
Research Direction ◽  
Geometric Representation ◽  
Visualization Method ◽  
Vector Representation ◽  
Basic Point ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability ◽  
The Given

Abstract Applying geometry to the analysis and interpretation of basic probability assignment(BPA) is a unique research direction in evidence theory. Though the geometric representation of BPA has been proposed, the visualization method of BPA is still lack of sufficient research. In this paper, we propose a new BPA visualization method based on the vector representation of the BPA to illustrate the image of BPA directly. The basic point and the uncertain vectors can be obtained by the given BPA firstly, and then we connect these components to construct the image of BPA. Through the image of BPA, we can effectively analyze the interaction effect of focal elements in BPA, and observe the potential characteristics of BPA directly. Meanwhile, the geometric meanings of parameters in the vector representation of the BPA can be explained. Finally, the advantages and applications have been studied and discussed.

scholarly journals Multiscale Probability Transformation of Basic Probability Assignment

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Meizhu Li ◽  
Xi Lu ◽  
Qi Zhang ◽  
Yong Deng
Keyword(s):  
Decision Making ◽  
Probability Function ◽  
Evidence Theory ◽  
Belief Function ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Probability Transformation ◽  
Basic Probability ◽  
Open Issue ◽  
Belief Model

Decision making is still an open issue in the application of Dempster-Shafer evidence theory. A lot of works have been presented for it. In the transferable belief model (TBM), pignistic probabilities based on the basic probability assignments are used for decision making. In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the plausibility function is proposed, which is a generalization of the pignistic probability transformation. In the multiscale probability function, a factorqbased on the Tsallis entropy is used to make the multiscale probabilities diversified. An example showing that the multiscale probability transformation is more reasonable in the decision making is given.

scholarly journals An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

scholarly journals Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 820
Author(s):  
Jingyu Liu ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Multi Agent System ◽  
Agent System ◽  
Combination Rules ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Reasonable Evidence ◽  
Basic Probability ◽  
Multi Agent

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.

Determination of Basic Probability Assignment Based on Recognition Sequence

2014 ◽  
Vol 1049-1050 ◽  
pp. 1171-1175
Author(s):  
Yan Fei Chen ◽  
Xue Zhi Xia ◽  
Kui Tu
Keyword(s):  
Target Recognition ◽  
Evidence Theory ◽  
Practical Application ◽  
Recognition Sequence ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Recognition Result ◽  
Basic Probability ◽  
Simulation Results

In some practical application of target recognition with sensors, the sensors will give the recognition sequence of targets, which is more detailed than the single recognition result. How to properly construct the basic probability assignment by the recognition sequence becomes the key to successful application of evidence theory. For the recognition sequence of the target recognition results of general sensor is incomplete, and the importance of the types in the recognition sequence is in descending order, this paper proposes a method to construct weights of recognition sequence, the basic probability assignments constructed by the weights are closer to the real recognition results. Simulation results show that this method is more reasonable and effective than the method of contrast.

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