Information theory based on fuzzy (possibilistic) rules

  • Arthur Ramer
6. Information
Part of the Lecture Notes in Computer Science book series (LNCS, volume 521)


The paper presents a systematic design of information theory based on fuzzy rules of combining evidence. The theory is developed both for discrete and continuous cases, represented respectively by finite distribution of possibility values and by an arbitrary measurable function on the domain of discourse.

Given a fuzzy set its associated uncertainty is expressed by an information function of the corresponding possibility distribution. Some natural properties of information make such expression of uncertainty essentially unique. A further extension is provided by the notion of information distance between two possibility distributions on the same domain of discourse. Finally, information functions and information distances are used to formulate a possibilistic principle of maximum uncertainty.

Key words

Fuzzy set information measure maximum uncertainty possibility theory 

Key words

Fuzzy information Information measures Information theory Possibility theory 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Arthur Ramer
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OklahomaNormanUSA

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