**Subject: **Fuzzy linguistic modifiers in expert systems

**From: **Otto Cordero (*ocordero@cti.espol.edu.ec*)

**Date: **Wed Oct 04 2000 - 12:18:05 MET DST

**sorted by:**[ date ] [ thread ] [ subject ] [ author ]**Next message:**Otto Cordero: "Fuzzy linguistic modifiers in expert systems"**Previous message:**Jorge Casillas: "Re: Grid-partitioning Fuzzy Systems"**Next in thread:**Otto Cordero: "Fuzzy linguistic modifiers in expert systems"

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Hi, i need to define rules for an expert system with fuzzy linguistic =

terms, the problem is that we dont know what the concepts involved in =

the rule are, since we have developed a technique to allow the expert to =

register his or her knowledge, all concepts are symbolic, for example:

if we have CONCEPT1 then we have a lot of of CONCEPT2

CONCEPT1 and CONCEPT2 are symbolic terms that define some knowledge of =

the expert. This rule is expresed in the form of a cognitive map. with =

an arrow from CONCEPT1 to CONCEPT2.=20

=20

How can i cuantify the presence of C2?. If we would know what C2 is we =

could define a fuzzy set, for example: VERY HEAVY=3D{ 0.6/80kg, =

0.8/90kg, 0.95/100kg }

but as i said C1 and C2 are symbolic. What we are doing now is using =

closed intervals between 0 and 1 to define the linguistic modifiers, for =

ex: high=3D0.7<x<=3D1.0; medium=3D0.4<x<=3D0.7 and so on.....but this =

goes in contrast with the escence of fuzzy logic and doesn't let us take =

advantage of fuzzy techniques.=20

with our aproach the rule would be reduced to:

if C1 then 0.8 of C2, this is what i need to improve defining really =

fuzzy sets instead of discrete weights.

I am asking you for suggestions in these theme. Or else if you know a =

paper that covers this issue let me know please.

Thank you

Otto Cordero

CTI - ESPOL=20

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Hi, i need to define rules for an =
expert system=20
with fuzzy linguistic terms, the problem is that we dont know what =
the=20
concepts involved in the rule are, since we have developed =
a technique to=20
allow the expert to register his or her knowledge, all concepts are=20
symbolic, for example:

if we have CONCEPT1 then we =
have __a lot=20
of__ of CONCEPT2

CONCEPT1 and CONCEPT2 are symbolic =
terms that=20
define some knowledge of the expert. This rule is expresed in the =
form of a=20
cognitive map. with an arrow from CONCEPT1 to CONCEPT2.

How can i cuantify the presence of =
C2?. If we=20
would know what C2 is we could define a fuzzy set, for example: VERY =
HEAVY=3D{=20
0.6/80kg, 0.8/90kg, 0.95/100kg }

but as i said C1 and C2 are symbolic. =
What we are=20
doing now is using closed intervals between 0 and 1 to define the=20
linguistic modifiers, for ex: high=3D0.7<x<=3D1.0; =
medium=3D0.4<x<=3D0.7 and=20
so on.....but this goes in contrast with the escence of fuzzy logic and =
doesn't=20
let us take advantage of fuzzy techniques.

with our aproach the rule would be =
reduced=20
to:

if C1 then 0.8 of C2, this is =
what i need to=20
improve defining really fuzzy sets instead of discrete =
weights.

I am asking you for suggestions in =
these theme. Or=20
else if you know a paper that covers this issue let me know=20
please.

Thank you

Otto Cordero

CTI - ESPOL

**Next message:**Otto Cordero: "Fuzzy linguistic modifiers in expert systems"**Previous message:**Jorge Casillas: "Re: Grid-partitioning Fuzzy Systems"**Next in thread:**Otto Cordero: "Fuzzy linguistic modifiers in expert systems"

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