Abstract
An axiomatic derivation of the U-uncertainty that is simpler than the standard proof which covers the case of sub-normal possibility distributions is presented. The standard deviation uses an extremely complex branching axiom which is equivalent to the branching axiom presented. While the terminology used is possibility theory, a fuzzy set can be considered as an unordered and sub-normal possibility distribution. As a consequence the correct measure of uncertainty for a fuzzy set is U(A) = ∫0h(A) log2|αA|+(1-h(A))log2|s(A)|.
| Original language | English |
|---|---|
| Pages (from-to) | 313-316 |
| Number of pages | 4 |
| Journal | Unknown Journal |
| State | Published - 1999 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Media Technology
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Uncertainty and subnormal possibility distributions. / Wierman, Mark J.
In: Unknown Journal, 1999, p. 313-316.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Uncertainty and subnormal possibility distributions
AU - Wierman, Mark J.
PY - 1999
Y1 - 1999
N2 - An axiomatic derivation of the U-uncertainty that is simpler than the standard proof which covers the case of sub-normal possibility distributions is presented. The standard deviation uses an extremely complex branching axiom which is equivalent to the branching axiom presented. While the terminology used is possibility theory, a fuzzy set can be considered as an unordered and sub-normal possibility distribution. As a consequence the correct measure of uncertainty for a fuzzy set is U(A) = ∫0h(A) log2|αA|+(1-h(A))log2|s(A)|.
AB - An axiomatic derivation of the U-uncertainty that is simpler than the standard proof which covers the case of sub-normal possibility distributions is presented. The standard deviation uses an extremely complex branching axiom which is equivalent to the branching axiom presented. While the terminology used is possibility theory, a fuzzy set can be considered as an unordered and sub-normal possibility distribution. As a consequence the correct measure of uncertainty for a fuzzy set is U(A) = ∫0h(A) log2|αA|+(1-h(A))log2|s(A)|.
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UR - http://www.scopus.com/inward/citedby.url?scp=0032591493&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0032591493
SP - 313
EP - 316
JO - Nuclear Physics A
JF - Nuclear Physics A
SN - 0375-9474
ER -