Path: utzoo!attcan!uunet!samsung!uakari.primate.wisc.edu!ames!pacbell!varian!zehntel!donw
From: donw@zehntel.zehntel.com (Don White)
Newsgroups: comp.ai.neural-nets
Subject: Re: What good are neural nets?
Message-ID: <4098@zehntel.UUCP>
Date: 21 Mar 90 20:38:20 GMT
References: <68764@aerospace.AERO.ORG>
Sender: usenet@zehntel.UUCP
Reply-To: donw@zehntel.UUCP (Don White)
Organization: Zehntel, Inc. Walnut Creek, CA
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In article <68764@aerospace.AERO.ORG> abbott@aero.UUCP (Russell J. Abbott) writes:
>
>Is there a good characterization of the kinds of problems for which
>neural nets are better than more traditional computational systems?
>More specifically:
>
     Yes, any system to which there may be more than one right answer.
     Or any poorly defined problem.(Almost the same thing.)

>1) Is there a recognized (or even suggested) set of criteria in terms of
>which one typically compares NN solutions to problems to more
>traditional computational solutions?  Two possible criteria I can think
>of are ease of development, e.g., training vs. programming, and speed of
>execution once a system is developed.
>
>2) Is there a characterization of a problem domain in which neural nets
>are superior under any such criteria?
>
 
>-- Russ abbott@itro3.aero.org

     A neural net appears to me to be a constrained chaotic system. (As is
  the human mind.) The constraints cause a predisposition to come up with
  a RIGHT answer. The chaotic aspect CAN result in a wrong answer BUT it can
  also result in an unexpected/unplanned answer. This is the key to creativity.

     I wonder how one would go about quantifying the fractional dimension of
  an neural net. Hmmmm.

     Don White
     Box 271177 Concord, CA. 94527-1177
     zehntel!donw