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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jian-Hong Wang Xin Li Zhan-Sheng Duan Chong-Zhao Han |
| Copyright Year | 2005 |
| Description | Author affiliation: Cold & Arid Regions Environ. & Eng. Res. Inst., Chinese Acad. of Sci., Gansu, China (Jian-Hong Wang; Xin Li) |
| Abstract | As an extension to fuzzy set theory, the vague set theory can remedy its shortage by describing the membership of the target of interest from two sides of both TRUE and FALSE, rather than only by a single membership value. Thus, the vague set is more powerful in the describing and processing of uncertain and inaccurate information, in particular of conflicting information. In this paper, the notions of the vague set are introduced first, and then based on the analysis of the limitations of the existing fuzzy data fusion methods, a new multi-attribute, multi-sensor and multi-target data fusion method based on the vague set is proposed. The new method organizes data, measures similarity and evaluates the results according to the vague set. Finally, an experiment is provided to illustrate the computational steps and performance improvement of the new method. Compared with the existing fuzzy data fusion method $fuzzy comprehensive evaluation, the new method is more efficient and powerful to fulfill multi-attribute, multi-sensor and multi-target data fusion with uncertain and inaccurate information. |
| Sponsorship | IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany |
| Starting Page | 2511 |
| Ending Page | 2517 |
| File Size | 390969 |
| Page Count | 7 |
| File Format | |
| ISBN | 0780390911 |
| DOI | 10.1109/ICMLC.2005.1527366 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-08-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fuzzy set theory Sensor fusion Fuzzy sets Decision making Probability Fuzzy systems Information technology Machine learning Intelligent sensors Set theory Decision Making Vague Set Fuzzy Set Data Fusion Information Fusion |
| Content Type | Text |
| Resource Type | Article |
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