I have been reading Fuzzy Belief Revision and trying to determine how the methods could be used to combine readings from conflicting sensors. For example, if one sensor reads 70 degrees and another reads 75 degrees.

This paper uses examples that combine subjectively defined properties. For example, a car may have the properties expensive, economical, and powerful with subjectively defined values 0.75, 0.50, and 0.75, respectively.

I can't see a way to apply these techniques to quantified data but this paper, and many other papers on Fuzzy Systems, make reference to applying Fuzzy techniques to "imprecise sensors".

Is there a paper that deals directly with combining sets of conflicting sensor readings using Fuzzy methods? Or otherwise combining quantified information rather than subjective properties?

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    $\begingroup$ People here may be able to answer your question, but you might get better results by asking your question on MetaOptimize.com/qa, as that site deals directly with machine learning and related topics. $\endgroup$ – Dave Clarke Apr 3 '11 at 10:33
  • $\begingroup$ Interesting. Thanks for the recommendation, Dave. That site will probably be extremely helpful to my research. It's fascinating how specialized the "stack" type sites get. I'm surprised this one isn't a part of the official stackexchange, though. $\endgroup$ – Chris Redford Apr 3 '11 at 16:47
  • $\begingroup$ Been a long time since I did anything with fuzzy logics, but I believe there is a lot of work by Didier Dubois and Henri Prade in this area. $\endgroup$ – Rob Apr 4 '11 at 15:52

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