From: mailbox@dmitry-kazakov.de   
      
   On Mon, 19 Apr 2004 09:04:40 GMT, "EarlCox"   
    wrote:   
      
   >After all the pages of discussion, you still just don't get it. You are   
   >still confusing fuzzy logic with the mathematical nature of the model's   
   >dependent to independent variable space. In a stochastic model the   
   >connections are made through samplings of random points (drawn from some   
   >distribution). In deterministic models the connection is made through   
   >linear or nonlinear functional transformations. Neither of these have   
   >anything to say about the underlying data representation which can be crisp   
   >or fuzzy. The logic we select reflects the kind of data we are handed.   
      
   See below.   
      
   >Simply put:   
   >   
   >Fuzzy logic addresses a universe of problems where the variables have   
   >imprecise (but possibly not uncertain) boundaries. As such, it should be the   
   >logic of first choice for such problems, whether they are deterministic,   
   >stochastic, or magical.   
      
   It is interesting why you want to call such problems deterministic or   
   stochastic. In my view such problems are inherently fuzzy, or   
   fuzzy/stochastic. If I have fuzzy data, there is no question how to   
   deal with that.   
      
   I mean other class of problems, ones with can be stated in   
   deterministic or stochastic terms, but still better to solve using   
   fuzzy approach. Others gave examples.   
      
   --   
   Regards,   
   Dmitry Kazakov   
   www.dmitry-kazakov.de   
      
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