One of the most important open issues is that the classical conflict coefficient in D-S evidence theory （DST） cannot correctly determine the conflict degree between two pieces of evidence. This drawback greatly limi...One of the most important open issues is that the classical conflict coefficient in D-S evidence theory （DST） cannot correctly determine the conflict degree between two pieces of evidence. This drawback greatly limits the use of DST in real application systems. Early researches mainly focused on the improvement of Dempster’s rule of combination （DRC）. However, the current research shows it is very important to define new conflict coefficients to determine the conflict degree between two or more pieces of evidence. The evidential sources of information are considered in this work and the definition of a conflict measure function （CMF） is proposed for selecting some useful CMFs in the next fusion work when sources are available at each instant. Firstly, the definition and theorems of CMF are put forward. Secondly, some typical CMFs are extended and then new CMFs are put forward. Finally, experiments illustrate that the CMF based on Jousselme and its similar ones are the best suited ones.展开更多
The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make （hard） decision, but for contrariwise to erroneous widespread idea/claim, this is not the only intere...The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make （hard） decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being’s subjective judgments. Therefore, a new probabilistic transformation of interval-valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results are provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler.展开更多
The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is...The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is not always available. In this case, an interval-valued belief degree rather than a precise one may be provided. So, the probabilistic transformation of imprecise belief function/mass in the generalized power space including Dezert-Smarandache （DSm） model from scalar transformation to sub-unitary interval transformation and, more generally, to any set of sub-unitary interval transformation is provided. Different from the existing probabilistic transformation algorithms that redistribute an ignorance mass to the singletons involved in that ignorance pro- portionally with respect to the precise belief function or probability function of singleton, the new algorithm provides an optimization idea to transform any type of imprecise belief assignment which may be represented by the union of several sub-unitary （half-） open intervals, （half-） closed intervals and/or sets of points belonging to [0,1]. Numerical examples are provided to illustrate the detailed implementation process of the new probabilistic transformation approach as well as its validity and wide applicability.展开更多
基金State Key Development Program for Basic Research of China (2007CB311006)Major Program of National Natural Science Foundation of China (6103200)+8 种基金National Natural Science Foundation of China (60572161, 60874105, 60904099)Excellent Ph.D. Paper Author Foundation of China (200443)Postdoctoral Science Foundation of China (20070421094)Program for New Century Excellent Talents in University (NCET-08-0345)Shanghai Rising-Star Program (09QA-1402900)Aeronautical Science Foundation of China (20090557004)"Chenxing" Scholarship Youth Found of Shanghai Jiaotong University (T241460612)Ministry of Education Key Laboratory of Intelligent Computing & Signal Processing (2009ICIP03)Research Fund of Shaanxi Key Laboratory of Electronic Information System Integration (200910A)
文摘One of the most important open issues is that the classical conflict coefficient in D-S evidence theory （DST） cannot correctly determine the conflict degree between two pieces of evidence. This drawback greatly limits the use of DST in real application systems. Early researches mainly focused on the improvement of Dempster’s rule of combination （DRC）. However, the current research shows it is very important to define new conflict coefficients to determine the conflict degree between two or more pieces of evidence. The evidential sources of information are considered in this work and the definition of a conflict measure function （CMF） is proposed for selecting some useful CMFs in the next fusion work when sources are available at each instant. Firstly, the definition and theorems of CMF are put forward. Secondly, some typical CMFs are extended and then new CMFs are put forward. Finally, experiments illustrate that the CMF based on Jousselme and its similar ones are the best suited ones.
基金State Key Development Program for Basic Research of China (2007CB311006)National Natural Science Foundation of China (60572161, 60874105, 60904099)Excellent Ph.D. Paper Author Foundation of China (200443)
文摘The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make （hard） decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being’s subjective judgments. Therefore, a new probabilistic transformation of interval-valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results are provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler.
基金supported by the National Natural Science Foundation of China (60572161 60874105)+5 种基金the Excellent Ph.D. Paper Author Foundation of China (200443)the Postdoctoral Science Foundation of China (20070421094)the Program for New Century Excellent Talents in University (NCET-08-0345)the Shanghai Rising-Star Program(09QA1402900)the "Chenxing" Scholarship Youth Found of Shanghai Jiaotong University (T241460612)the Ministry of Education Key Laboratory of Intelligent Computing & Signal Processing (2009ICIP03)
文摘The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is not always available. In this case, an interval-valued belief degree rather than a precise one may be provided. So, the probabilistic transformation of imprecise belief function/mass in the generalized power space including Dezert-Smarandache （DSm） model from scalar transformation to sub-unitary interval transformation and, more generally, to any set of sub-unitary interval transformation is provided. Different from the existing probabilistic transformation algorithms that redistribute an ignorance mass to the singletons involved in that ignorance pro- portionally with respect to the precise belief function or probability function of singleton, the new algorithm provides an optimization idea to transform any type of imprecise belief assignment which may be represented by the union of several sub-unitary （half-） open intervals, （half-） closed intervals and/or sets of points belonging to [0,1]. Numerical examples are provided to illustrate the detailed implementation process of the new probabilistic transformation approach as well as its validity and wide applicability.
