Comparative study of type-2 fuzzy sets and cloud model

  • Authors:

  • Kun Qin;Deyi Li;Tao Wu;Yuchao Liu;Guisheng Chen;Baohua Cao

  • Affiliations:

  • School of Remote Sensing Information Engineering, Wuhan University, Wuhan, China;The Institute of Beijing Electronic System Engineering, Beijing, China;School of Remote Sensing Information Engineering, Wuhan University, Wuhan, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China;The Institute of Beijing Electronic System Engineering, Beijing, China;Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA

  • Venue:
  • RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
  • Year:
  • 2010

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Abstract

The mathematical representation of a concept with uncertainty is one of foundations of Artificial Intelligence. Type-2 fuzzy sets study fuzziness of the membership grade to a concept. Cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. The two methods both concentrate on the essentials of uncertainty and have been applied in many fields for more than ten years. However, their mathematical foundations are quite different. The detailed comparative study will discover the relationship between each other, and provide a fundamental contribution to Artificial Intelligence with uncertainty.