원문정보
초록
영어
Evidence theory is an important method for uncertainty reasoning. Because evidence theory can well process uncertain information, imprecise information, fuzzy information, and information without prior knowledge, it is wildly used in information fusion, expert system, fuzzy recognition, and intelligent decision system. This paper depend on original evidence theory fusion algorithm, put forward a new algorithm to process conflict information efficiently, this algorithm change the basic probability assignment of evidences by changing its reliability, and then get the ultimate decision making result by doing a new combination. The simulation results show the effectiveness of this algorithm, and it can give a better decision making results in processing conflict evidences.
목차
1. Introduction
2. DS Combination Rule
3. Other Evidence Theory Algorithm
3.1. Yager
3.2. SUN Quan Combination Rule
3.3. Murphy Method
3.4. Pearson Correlation Coefficient
4. Cosine Similarity Coefficient
4.1. The Cosine Similarity Coefficient
4.2. The Weights of Evidences
4.3. Reallocate the Basic Probability Assignment Function
4.4. Combination Rule
5. Simulation and Analysis
6. Conclusion
Acknowledgements
References