广义证据理论中的基本概率指派生成方法

邓勇 1; 3; 韩德强 2

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广义证据理论中的基本概率指派生成方法

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- 广义证据理论中的基本概率指派生成方法
- Methods to Determine Generalized Basic Probability Assignment in Generalized Evidence Theory
- 西安交通大学学报 2011年45卷第2期页码：34-38
- 作者机构：
  
  1. 西南大学计算机与信息科学学院,重庆,400715
  2. 上海交通大学电子信息与电气工程学院,上海,200240
- 作者简介：
- 基金信息：
  
  : 2010-04-02. 作者简介: 邓勇(1975-),男,教授,博士生导师. 基金项目:国家重点基础研究发展规划资助项目(2007CB311006);国家自然科学基金资助项目(60874105,60904099);教育部新世纪优秀人才基金资助项目(NCET-08-0345);上海市青年科技启明星计划资助项目(09QA1402900);上海市教育委员会重点学科建设资助项目(J50704);重庆市自然科学基金资助项目(CSTC, 2010BA2003)
- DOI：
  中图分类号： TP391
- 网络首发：2011-02-10，
  
  纸质出版：2011
- 稿件说明：
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邓勇 1, 3, 韩德强 2. 广义证据理论中的基本概率指派生成方法[J]. 西安交通大学学报, 2011,45(2):34-38.

Methods to Determine Generalized Basic Probability Assignment in Generalized Evidence Theory[J]. 2011, 45(2): 34-38.
邓勇 1, 3, 韩德强 2. 广义证据理论中的基本概率指派生成方法[J]. 西安交通大学学报, 2011,45(2):34-38. DOI：

Methods to Determine Generalized Basic Probability Assignment in Generalized Evidence Theory[J]. 2011, 45(2): 34-38. DOI：

摘要

鉴于基本概率指派生成对证据理论的研究具有重要的意义

文中针对辨识框架不完整情况提出了一种强约束广义基本概率指派赋值方法

新方法可以生成空集不为零的广义基本概率指派赋值

该数值的大小反映了系统是开放世界的可能性.针对辨识框架完整情况提出了一种弱约束基本概率指派赋值方法

该方法在样本与表示模型之间不相交时

也可以给出样本与模型相似性度量的数值

根据所提出的策略生成弱约束的基本概率指派. 算例表明了所提出方法的有效性.

Abstract

Determination of the basic probability assignment(BPA)is the first step to use evidence theory. How to determine BPA is still an open issue. Two methods

namely strong constrain method and weak constrain method

are proposed to deal with open world and close world respectively. In the framework of the proposed generalized evidence theory

a strong constrain method for determination of generalized basic probability assignment(GBPA)is developed. Compared with the existing methods

the new method can generate GBPA without the constrain that the empty set should be set zero. In addition

the value of the empty illustrates the possibility degree that the frame of discernment is not complete. A weak constrain method is also presented to generate BPA in the close world. The weak constrain can determine the BPA even the sample does not cross with the representation model. Some numerical examples are used to illustrate the efficiency of the proposed methods.

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Keywords

references

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韩崇昭, 朱洪艳, 段战胜. 多源信息融合[M]. 北京:清华大学出版社, 2006.

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邓勇,蒋雯,韩德强. 广义证据理论的基本框架 [J].西安交通大学学报,2010,44(12):119-124.

DENG Yong, Jiang Wen, HAN Deqiang. Basic frame of generalized evidence theory [J]. Journal of Xi'an Jiaotong University, 2010,44(12):119-124.

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DENG Yong, SHI Wenkang, DU Feng, et al. A new similarity measure of generalized fuzzy numbers and its application to pattern recognition [J]. Pattern Recognition Letters, 2004, 25(8): 875-883.

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