# Re: Grid-partitioning Fuzzy Systems

Subject: Re: Grid-partitioning Fuzzy Systems
From: Jorge Casillas (J.Casillas@decsai.ugr.es)
Date: Wed Oct 11 2000 - 01:51:22 MET DST

--------------5A847703274CF428D025BE6D
Content-Type: text/plain; charset=us-ascii
Content-Transfer-Encoding: 7bit

Hi Robert!!

In my opinion, you could understand "grid-partitioning" from two different
angles:

* Grid-partitioning when LEARNING the Fuzzy System: The operation mode of some
learning approaches involves using a grid (wich can be fuzzy or crisp) defined by
the input variable fuzzy partitions to bracket the example data set into
subspaces and obtaining one or several fuzzy rules representing the behavior of
such subspaces exclusively considering the examples located in the corresponding
subspace (the called positive examples). The below figures show this process.

[Image]
[Image]

The first figure illustrates a fuzzy grid and the number of rules where a
specific example will contribute to derive it. The examples lying in white zones
have an influence on the generation of one rule, the ones lying in light grey
zones influence two rules, and the ones lying in dark grey zones influence four
rules. Once obtained the group of example to consider in each region, the second
figure illustrate the learning process.

* Grid-partitioning as one of the interesting features developed by Fuzzy
Systems: Another point of view is refered to one of the most interesting features
developed by a Fuzzy System, the interpolative reasoning. This characteristic
plays a key role in the high performance of Fuzzy Systems and is a consequence of
the cooperation among the fuzzy rules composing the Knowlege Base. As it is
known, the output obtained from an Fuzzy System is not usually due to a single
fuzzy rule but to the cooperative action of several fuzzy rules that have been
fired because they match the system input to any degree. This fact is due to the
grid-partitioning.

Best regards,

Jorge Casillas

robert_wilhelm_land wrote:

> Would someone kindly give a practical example for the usage of a
> Grid-partitioning Fuzzy System?
>
> Thinking of "grid" - I would associate something in the direction of
> image processing/recognition.
>
> Even any short comment would be helpful
>
> Robert
>
> ############################################################################
> This message was posted through the fuzzy mailing list.
> (1) To subscribe to this mailing list, send a message body of
> "SUB FUZZY-MAIL myFirstName mySurname" to listproc@dbai.tuwien.ac.at
> (2) To unsubscribe from this mailing list, send a message body of
> "UNSUB FUZZY-MAIL" or "UNSUB FUZZY-MAIL yoursubscription@email.address.com"
> to listproc@dbai.tuwien.ac.at
> (3) To reach the human who maintains the list, send mail to
> fuzzy-owner@dbai.tuwien.ac.at
> (4) WWW access and other information on Fuzzy Sets and Logic see
> http://www.dbai.tuwien.ac.at/ftp/mlowner/fuzzy-mail.info
> (5) WWW archive: http://www.dbai.tuwien.ac.at/marchives/fuzzy-mail/index.html

```--
----------------------------------------------------
Jorge Casillas
Dep. of Computer Science and Artificial Intelligence
Phone (office / home): +34-958-242376 / 257614
Fax (office): +34-958-243317
mailto:J.Casillas@decsai.ugr.es
http://decsai.ugr.es/~casillas
--------------5A847703274CF428D025BE6D
Content-Type: multipart/related;
boundary="------------31626C68DD1F5865F8022D97"
--------------31626C68DD1F5865F8022D97
Content-Type: text/html; charset=us-ascii
Content-Transfer-Encoding: 7bit
<!doctype html public "-//w3c//dtd html 4.0 transitional//en">

Hi Robert!!
In my opinion, you could understand "grid-partitioning" from two different
angles:
* Grid-partitioning when LEARNING the Fuzzy System: The operation mode
of some learning approaches involves using a grid (wich can be fuzzy or
crisp) defined by the input variable fuzzy partitions to bracket the example
data set into subspaces and obtaining one or several fuzzy rules representing
the behavior of such subspaces exclusively considering the examples located
in the corresponding subspace (the called positive examples). The below
figures show this process.

The first figure illustrates a fuzzy grid and the number of rules where
a specific example will contribute to derive it. The examples lying in
white zones have an influence on the generation of one rule, the ones lying
in light grey zones influence two rules, and the ones lying in dark grey
zones influence four rules. Once obtained the group of example to consider
in each region, the second figure illustrate the learning process.

* Grid-partitioning as one of the interesting features developed by
Fuzzy Systems: Another point of view is refered to one of the most interesting
features developed by a Fuzzy System, the interpolative reasoning. This
characteristic plays a key role in the high performance of Fuzzy Systems
and is a consequence of the cooperation among the fuzzy rules composing
the Knowlege Base. As it is known, the output obtained from an Fuzzy System
is not usually due to a single fuzzy rule but to the cooperative action
of several fuzzy rules that have been fired because they match the system
input to any degree. This fact is due to the grid-partitioning.
Best regards,
Jorge Casillas

robert_wilhelm_land wrote:
Would someone kindly give a practical example for
the usage of a
Grid-partitioning Fuzzy System?
Thinking of "grid" - I would associate something in the direction of
image processing/recognition.
Even any short comment would be helpful
Robert
############################################################################
This message was posted through the fuzzy mailing list.
(1)  To subscribe to this mailing list, send a message body of
"SUB FUZZY-MAIL myFirstName mySurname" to listproc@dbai.tuwien.ac.at
(2)  To unsubscribe from this mailing list, send a message body
of
"UNSUB FUZZY-MAIL" or "UNSUB FUZZY-MAIL yoursubscription@email.address.com"
to listproc@dbai.tuwien.ac.at
(3)  To reach the human who maintains the list, send mail to
fuzzy-owner@dbai.tuwien.ac.at
(4)  WWW access and other information on Fuzzy Sets and Logic
see
http://www.dbai.tuwien.ac.at/ftp/mlowner/fuzzy-mail.info
(5) WWW archive: http://www.dbai.tuwien.ac.at/marchives/fuzzy-mail/index.html
--
----------------------------------------------------
Jorge Casillas
Dep. of Computer Science and Artificial Intelligence
Phone (office / home): +34-958-242376 / 257614
Fax (office): +34-958-243317
mailto:J.Casillas@decsai.ugr.es
http://decsai.ugr.es/~casillas

--------------31626C68DD1F5865F8022D97
Content-Type: image/jpeg
Content-ID:
Content-Transfer-Encoding: base64
Content-Disposition: inline; filename="C:\TEMP\nsmailDC.jpeg"
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--------------31626C68DD1F5865F8022D97
Content-Type: image/jpeg
Content-ID:
Content-Transfer-Encoding: base64
Content-Disposition: inline; filename="C:\TEMP\nsmailGN.jpeg"

Hh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwh
MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAAR
AgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK
FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG
h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl
5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREA
AgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYk
NOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE
hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk
CiiigAooooAKKKKACiiigAooqOSeGF4UlljR5n2RKzAF22lsL6narHA7AntQBJRWPqHiKCw1
yw0j7LPNdXuSmx4kAUdSPMdS+ACSIw7KBkgblzsUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU
AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR
RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFU7zVbHT7i1t7u5jilun2Qq38RyB+A
3Mi5OBudF6soNygAooooAKKpx6rYy6pNpkdzG15Cm94h1A4z7EgMhIHIDoTgOublABRRRQAU
VT03VbHV7drjT7mO4iVyhZPXAI/AqVYHoysrDIYE3KACvO/FltrWu69eSaJYWl4dGt40i867
MB+1+ZFdeWV2MJBiG1P3oxiRhuzynolZfiPX7Hwt4fvNa1JpBaWqBn8tdzMSQqqB6liBzgc8
kDmgDH1zU/DcWjW3jC9ku7m0W3W5tYo5Zis4CmVCLfcEZwAXyy5XZuJUJlesrHv4bfxd4NuY
bO622ur6e6Q3Hlk4SWMhX2nB6MDg4/CtigAooqnearY6fcWtvd3McUt0+yFW/iOQPwG5kXJw
NzovVlBALlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU
AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABV
e+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASasVz8X/FR6stz10iwlkjWN+lzdRyBd5H
ZYmRwN2dzncAPLjdgAtdE/tSCe71+33XdzuVIt/NpDv3RohU/JINsbs6sT5ighiqR7bGj31w
kr6Tqkm7UYd7RyFQBdQBsLKMYBYAoJAANrn7oVkLbFZetabNeJDdWDRxanaOHgkckBl3KZIm
IB+SRV2nhsHa4Usi0AalZet6lNZ2/wBm09Y5dXuUcWcLgldwH35MEERKSpY+4AyzKpItfsX8
Pz61K0kFpbJM1yJEy0JiLLKpC5yVZGHy5BxwSCCY9Jsbh7yfV9Sj2Xc/y28LMGNnBhf3WR8u
5mXe5XuQu51jRqAK9z4b8jTml0uTGtxb5ob64b5ppmAyJio+aN9qKygYCquwKUj26mm6lDqd
u0kayRSRuY57eUASQSAAlHAJGcEEEEgghlJUgm5XP6t/xILyXxDH8tgImbVYk6lVC4uAO7Rq
pBAwzIerGNEIB0FYepTzapfro9hLIkaOP7TuImKGKMoSIkcciViUJxysZLZRmjJuatqf9mxQ
JFD595dy/Z7WAtsEkm1n+ZsHaoVHYnBOFOAzYUmjaZ/ZWnJDJN9pu3xJd3ZXa1zNgBpCMnGc
yxzQSoHjkjYMrqRkEEcEEc5qSufsf+Ke1OPSn4029lK6YF6W7CMs1vjqFwkjofugZTCBUDAH
UqRy65qEc2z7JbM2fKBGf3siqflYYKt0IJK95YWNvpmnW1hZx+Xa2sSQwpuJ2ooAUZPJwAOt
AHm+k/8AFrfFsXh6T7dJ4V1qVV0qV/3iWF0zNm3J5ba+QQT09D8716hWX4j0Cx8U+H7zRdSW
Q2l0gV/LbaykEMrA+oYA85HHII4rm/h/r+qP5/hTxNB5Ov6REgMpl3rfwcqtwhY7m6YY9mIz
gkqoB2F9fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk1j2uif2pBPd6/b7ru53KkW/m0h
MfR764SV9J1STdqMO9o5CoAuoA2FlGMAsAUEgAG1z90KyFtisvWtNmvEhurBo4tTtHDwSOSA
y7lMkTEA/JIq7Tw2DtcKWRasaXqUOrWC3cKyIC7xvHIAGjkRyjocEjKsrLkEg4yCRg0AXKKK
KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA
ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoorH8TeIrPwxo5v7yWBN8sdv
CJ5hEjSOwVcseijJZiASFVjg4xQBHrU817ew6DZSyRSzIJ7u4jYgw24dQVyOVeX5kU5UgCRl
OYwDsQQQ2tvFb28UcMESBI441CqigYAAHAAHGK5PR/FHg/S7N0k8Y6HcXU8r3FzOb6FTJIxy
eN5IUDCKCSVRFXJxmtD/AITvwf8A9DXof/gxh/8AiqAOgorn/wDhO/B//Q16H/4MYf8A4qsf
XPHfhu88vRrPxXpVv9tik86/j1GMfZoxtDbGDcTHeNmcAYZudm1gA1Ca3OuXGtC13aNZXccV
8RIQk067ALrb9wrASFZieNjE4NtHnuK5uDxp4JtbeK3t/Evh+GCJAkccd/CqooGAAA2AAOMV
EheSSRgqooGSSTwABzmsP/hO/B//AENeh/8Agxh/+KrDm8aeFda1sJP4l0aHT9LuFcK9/F/p
k2wMp5bBiTeCDyTKgPy+X84BY8NKtjqls11YSWkeo27NpUczNmxiG1jaFWwEcj95tXPCMn3L
dCe0rk9V8U+CdX0u4sJ/FujIkyYEkWpQh4m6q6Ek7XVgGU9iAe1R6T8RfDdxFPDf+I9DjurW
XyXkS+jWKf5VYSR5bO0hgCMnawZctt3EA7CuP+I+v2+j+HBaCCC81bUpUttMsZJTGZpy6hWD
AgrsJV9wK4IXDKSDVyf4g+Dba3lnfxVoxSNC7CO9jdiAM8KpJY+wBJ7VwfgvX9E1bXZ/HPiv
XPDlvqdxEsOmWRvoi+nW3zHDEt/rG3HORkcj5dxRQDqPhroNv4b06+02eL/ifrKsmqXTzGV7
x3G5Zg7BWaM5ZRkDDLIPmIZm7iuL1fxT4VuntL6x8W+H01Cydnh8zUolSVWXDxOwOQjcHjOG
d4qsImuorlLprfEAODDI6Yb97ucINyfdlIdcMG3NU+I/hWxsGmt/EGjXc5dI44U1GIZZ3CAs
QSVQFss2DtUMcHGKj0fxR4P0uzdJPGOh3F1PK9xczm+hUySMcnjeSFAwigklURVycZoA0PBP
iHS/E3hLT9Q0hYIrfylja1hPFq6qMw4wMbeAOBkYI4IroK8b1fxLoPg7xr/wlHh/W/D9xpOq
vFDrtlb3SNKG3HF0gUksRvO5VHPJIJYsvon/AAnfg/8A6GvQ/wDwYw//ABVAHQVz+q/8SHUR
rsfy2MmI9TjXgclFS5PYeWAQ54/dnJJ8pVo/4Tvwf/0Neh/+DGH/AOKo/wCE78H/APQ16H/4
MYf/AIqgDoKK4vw34l0GDWY/DWna3pt/BMk1xYfZrpHMShgWgKqcAKH/AHYX/lmhXA8vc/aU
AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR
RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFRzzw2tvLcXEqQwRIXkkkYKqKBkkk8
pklmLMessbCz0yzjs7C0gtLWPOyGCMRouSScKOBkkn8ax/EP/Ic8J/8AYVk/9IrqgDoKKKKA
CiiigAooooAKKKKACiiigDL8SyWMPhXV5dUhkn09LKZrqKM4Z4gh3qORyVyOo+orUqnq0l9D
o19LpcMc+oJbyNaxSHCvKFOxTyOC2B1H1FXKACiiigAooooAy/EsljD4V1eXVIZJ9PSyma6i
jOGeIId6jkclcjqPqK1Kp6tJfQ6NfS6XDHPqCW8jWsUhwryhTsU8jgtgdR9RVygAooooA5/x
D/yHPCf/AGFZP/SK6roK5/xD/wAhzwn/ANhWT/0iuq6CgAooooAKKKKACiiigAooooAKKKKA
CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo
ooAKKKKACs/U9Yt9L8qN0nuLqfPkWttEZJJCMZ4HCrkqC7FUUsu5hkVoVT1LSrHV7dYL+2jn
RHEkZbhonAIDow5Rxk4ZSCOxFAGfBok19cRXviH7JdzwuJLW2jjJgtGByGG7JeUHjzSF4Hyq
m5925XP/ANpXnh/5Ndm+02J/1epRW5HlgdrgLkLwNxmAWP72RHhd/QUAFc/4h/5DnhP/ALCs
n/pFdV0Feb+IvHuj3Hj3w/oGlCfVdWstQmluLazCDZttZlK75GVC2ZOgbjYwOCACAekUVTkv
Z01SG0XTLuSB03Neq0XlRnn5WBcPngdFI+Yc9cRxajdSfbt2jX0f2bPlbng/0vGf9XiQ4zgf
f2feHTnABoUVlyareJpcN2vh/UpJ3fa1kslv5sY5+ZiZQmOB0Yn5hx1xYkvZ01SG0XTLuSB0
3Neq0XlRnn5WBcPngdFI+Yc9cAFyis+LUbqT7du0a+j+zZ8rc8H+l4z/AKvEhxnA+/s+8OnO
I5NVvE0uG7Xw/qUk7vtayWS382Mc/MxMoTHA6MT8w464ANSiqcl7OmqQ2i6ZdyQOm5r1Wi8q
4btfD+pSTu+1rJZLfzYxz8zEyhMcDoxPzDjrixJezpqkNoumXckDpua9VovKjPPysC4fPA6K
R8w564AI9dj87w9qUX9of2bvtZV+3btv2bKH95nIxt+9nI6dRWhXP6zqMEnh7xD/AGzo19Hp
ltaTedueL/S4Qjb/AC9khYZUfxbD8w6c4pf8JvN/0Keuf992f/yRQB1tFcl/wm83/Qp65/33
Z/8AyRR/wm83/Qp65/33Z/8AyRQB1tFcl/wm83/Qp65/33Z//JFH/Cbzf9Cnrn/fdn/8kUAb
uuxed4e1OL+0f7N32kq/bt237NlD+8zkY2/ezkdOorQrhNb8aW0mg6imq+EtcOnNayi6HmWv
+q2nf924z93PTn0ru6ACiiigDn/EP/Ic8J/9hWT/ANIrqugrn/EP/Ic8J/8AYVk/9IrqugoA
KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii
igAooooAKKKKACiiigAooooAKKKKACiiigAorPtdasr7UZ7K2aeSSHcHkFtJ5OVO1lEu3y2Y
HgqGJBDAjKnGhQAVyeqiHwLpdxq1hJaWmh2qebc6cyBE9P8ARyCBG7Z+5gq7YA2M7OTxn8QN
I8Gxpbzb7zWLlM2WmW6lpbhiwVRwDtBY4yeuG2hiMVz+mfD/AFLxTqkfiD4k/ZLu4iRBZaRb
M32W0+6zFhn53LDBBLKQOrDaFAM9tV1r4xPe2Oh3MemeCo7gWt3ejIu70BSXSMdEQhkHzAHB
tykH7ygOwB2lFFFABRRRQAUUUUAFFFFABRRRQBz/AI7/AOSeeJf+wVdf+imrMrT8df8AJPfE
v/YKuv8A0U1ZlABRRRQAUUUUAY3i/wD5ErXv+wdcf+i2r0yvM/F//Ila9/2Drj/0W1emUAFF
FFAHP+If+Q54T/7Csn/pFdV0Fc/4h/5DnhP/ALCsn/pFdV0FABRRRQAUUVXvr6306zkurqTy
4UwCQpYkkgKqqMlmJIAUAkkgAEmgCxRVexvYtQs47qFJ0jfOBPA8LjBI5RwGHTuOevSrFABR
RRQAUUUUAFFFFABRRVPUtW03RrdbjVNQtLGBnCLJdTLEpbBOAWIGcAnHsaALlFFFABRRRQAU
UUUAFFFFABRRVe+v7PTLOS8v7uC0tY8b5p5BGi5IAyx4GSQPxoAsUVHBPDdW8VxbyxzQSoHj
kjYMrqRkEEcEEc5qSgAooooAKKKKACiiigAooooAKKp6lq2m6NbrcapqFpYwM4RZLqZYlLYJ
wCxAzgE49jVygAooooAK830rVL0+NE1uSxvk0zU7uazS6eSP7MYsItu6sHLFWeBimUVc3rbW
OQZPSKriws1s4bMWkAtYdnlQiMbI9hBTavQbSqkY6YGOlAHN6T/Ytv4x1CaPULufUL12iLyW
rkEfIcqRkjoIbPXp/FhvNSs9NfT4XYWTJeuXgXaRv8owgNK2SCd/yqSq9XMnQTwQ3VvLb3EU
c0EqFJI5FDK6kYIIPBBHGKAOT8C+BtC8L2Y1Gxl/tLUr6IPc6xJIZHu9x3lgckBWJzx1wuSx
Ga7CvK5/Cvib4bmW/wDBE0mq6GqF59AvZWdox5m4/ZSOh2s3ByTjJ8xiAO08J+NNC8a6c15o
t35vl7RPC6lJIWYZAZT+IyMqSDgnBoA6Cuf8Q/8AIc8J/wDYVk/9IrqtyeeG1t5bi4ljhgiQ
vJJIwVUUDJJJ4AA5zXH6hqs2q+JPCs1tbRnSDqcnk3jSkNO32O5+ZE24MRGcOWG7GQpUq5AO
0ooooAKKKKACiiigAooooAKKKKAOf8d/8k88S/8AYKuv/RTVyfiMTtb2ccRmZHucS29vcCCa
4Xy3OyN9y4YMFc4ZflRuvQ9Z47/5J54l/wCwVdf+imrCvtPttRgWK5VyFbejxyNG6NgjKupD
KcEjgjIJHQmgDJ0e/tbHTr+S8vJrW3trkI8epTAtaZRCEaUuwfJYODuOPMC8bcDa+0pJZfar
YfakaPzIhCynzRjI2kkLz2JIHPWsi+8OJJpa2enyJBJ53nG4uDNNKW2ld3mCVJN2MLu3/d+X
pxW1CjRQRxvK8rKoUyOAGcgdTgAZPsAPagDJ/tvUP+hX1f8A7+2n/wAfo/tvUP8AoV9X/wC/
tp/8frZooA4/xTq99L4Q1qN/DmqRK1hOpkeS2KoDG3JxMTgewJ9q9D/4SHVP+hM1z/v9Zf8A
yRXJ+L/+RK17/sHXH/otq9MoA5//AISHVP8AoTNc/wC/1l/8kUf8JDqn/Qma5/3+sv8A5Iro
93v0qnoVlLrVnrlkr6rp2mvstDbahOl3cxzKW84nzTMPLdGhAViQRlgoDBm7C4sbe7ntJp49
8lpKZoDuI2OUaMnjr8rsOfX1xRDY29veXN1FHsmudpmIY4cqMBivTdjALYyQqgkhVwAc3qWp
Q+CNL03T7e50ZItjKp1jVhZk7cfcAiYEfN91QqoNoUAYA3NE1L+19HgvvMsZPN3fNYXX2mE4
Yj5ZNq7unPAwcjtmtCigDy9ri8vtR02CWPXNRjaXXC0GnaibZ/3d+iRlm86PKqpKgbjjIwMd
PTIIVtreKBDIUjQIpkkZ2IAxyzElj7kknvVO10TTrO6jube32TR/aNjb2OPPlEsvBP8AE6g+
2MDA4rQoA5fW9Z1HRtRnto3gmbUIlGliYqFiuMiMiTBU+Tl4jnk7mdc7nhRukgjaG3iieaSd
0QK0sgUM5A+8doAyevAA9AKz7jQLG8fVWu1knGqW62lwrNtHkBWAjXbggZkkOfvZc84CgZ+r
i7tdY8IIupXbIb2SC4UlFFz/AKJOwaTaoyQyA4GFyc44XABc1m8e21Xw9Egyt1qDxP8AOwwB
cWNvdz2k08e+S0lM0B3EbHKNGTx1+V2HPr64qnpvh7TNJuGns4JFcoY0DzySLChIJSJWYiJO
iq30fVNJ1KO2gKCeO6WGQTtE4VhsYOgUEAEpIroSF3IduDsDTYRrL6ozO85t1t0ViCsShizF
roeL7bTXeCe1u7uW0RYLKcLEUjkfcbo/upG/dFWjUAqzEZPlnd2FY6+FtGTWI9VFn/pkUrzR
Dwo2YI/eferY0ObWB4y1i01a5gm8rT7J4zbB0jbdJc5by2LbGO0KcM2QinIztXUj8OaRFb2l
uLGNoLSybT4Y5CXUW7BA0ZDEhgRGg+bJ49zk0rw/p+jXFxcWn2tp7hESWS6vZrliqFioBldi
AC7nAx1NAFPX9Zl8O3kN/cP5mlyRPFJCCgdJlBdGXcV+UqJA5JIXajHYiyONTSo76LS7ddTm
jmvym64aIYQOeSqcA7AThc84AySckh02F9ZTVHaRp47dreJSRtjVmDORgZJYpHnJONgxjLZ4
/wAVHTfC3h/StMTX5NNkD/ZtNEt4sKx4IxI5+XzEgjzhHO18BX3OymgDpPEt49jpUEsYyzah
ZRH52Xh7qJDypB6MeM4PQggkHPs9d1N/FDWN6ILaFpZI47aW0mRiqhijpcnMUzMqh/KUKyqz
ZOY2B6C9sbfUIFhuo/MjWWOYDcRh43WRDx6Mqn3xzxVOHw9plvqh1GOCQT72kVDPIYo3bO50
iLbEc7myyqCd75PzNkAp+MNcTQtHhdr+Cxku7uK1S4ndUWMM2ZGDP8gZYlkZd3BZQMMSFOfC
Z/HfgbS7uC7sYbt9ryzokrpHMoZJRC8UyMPn3qHVyCuR8ytmukn02G51Szv5WkZ7RJBFGSCg
Z9o34x98KGUEdBI4/irm/FX9l+GvD2o3dzqE9tDc3ZmjtlvvsizTsmBCki4MSu48xmBHzF2Y
7S4IB0k2nLdaWLC6ubuQbFV50naCVyMHdvi2FSSOduByRjHFefxwy6Z4Sm1W2udZubxdd+yB
G1SaUvEuqiMRqssmwEooTJxkE5OCc+gaSzNo1iz38eoObeMtexqqrcHaP3gC8AN1wOOeKj/s
TTvsP2L7P/o/2v7bs3t/rvO8/dnOf9Z82OnbGOKAOH8V6/qz/wBoYsNV0byvDWqzp508Q3yL
5G118mV8MmTgnBG7jvUt+k2neFvEl3baX4n0yeLR7po7i/1g3CqwQkbQLmQq+RkNtGMHkZwe
z1LRNO1ff9ut/N32k1k3zsuYZtvmLwR12Lz1GOCOapp4S0pbe7t3bUriC7t3tpo7rVLmdWjc
YYAPIQDjjcMEc4PNAGO2q30Wu6Fo15cyG+g1NhMyfKLq2a2uzC7bcAk+V8y4AEkbEKF2E9Jr
H9opZpPpnzzwypI0B24uI84dMt0YqSV5Ubwu47dwMl1pVje39jf3FtHJd2Du9rMfvRF0KNg+
hU8jpwD1AIuUAY+gX1xq8U2rGT/iXXextOj2gEwbQRK3cM5LHBPCCPKq28Vj3et6nbya79ih
z9n1WO3abyJrvyIzaQyb/IRtz5dgm2PaBv3kHDk9JpOmw6No1jpdu0jQWVvHbxtIQWKooUE4
AGcD0FV7nw9pl2LnzIJFe5uBdPLFPJHIJRGsQdHVgyHYoU7SMjIP3jkAk0S+fUdHguZZIJJG
3KzwKyqSrFTlH+aNuPmjbJRsqSSuTz/izVLi08Q6RZLqeq2NrPaXUsh0ywF1I7o8AXK+TKQo
Ej84HJHPSuosbG306zjtbWPy4UyQCxYkkkszMclmJJJYkkkkkkms/VtBOp6jaX8Gq32nXVrF
LCr2ohO5JDGWBEkbjrEvTHegDn7C2v8Axfoumagmo/ZZrW7uoTdNayR3TxxztGp4dAjMIlZ0
dGQsBmPC7a7iqel6bDpNgtpC0jgO8jySEFpJHcu7nAAyzMzYAAGcAAYFXKACiiigAooooAKK
KKACvN/iF4Xs21FNd0XVZ9I8ZSRGOyW0kAbUWQoRHJH/AMtFGFBY/KqkF8quB1k+tzX1xLZe
Hvsl3PC5jurmSQmC0YHBU7cl5QefKBXgfMybk3XNM0lNN82V7me8vJsCa7udvmSBc7V+VVVV
XJwqgDJY43MxIB53oni2a48Y/wDCP/EKaOw1mK4Q6fZQxlbC77xzK5yzPuDYDMF3bML5i/L2
niH/AJDnhP8A7Csn/pFdVY8T+GNL8XaHNpGrwebbycqy8PE46Oh7MMn8yCCCQfO2sfGnhPxT
4esruaTxJoK6nPLZTGRTqAY2s2I5GkdEYANIc5/g6r8qUAeuUVTkvZ01SG0XTLuSB03Neq0X
lRnn5WBcPngdFI+Yc9cRxajdSfbt2jX0f2bPlbng/wBLxn/V4kOM4H39n3h05wAaFFZcmq3i
aXDdr4f1KSd32tZLJb+bGOfmYmUJjgdGJ+YcdcWJL2dNUhtF0y7kgdNzXqtF5UZ5+VgXD54H
RSPmHPXABcorPi1G6k+3btGvo/s2fK3PB/peM/6vEhxnA+/s+8OnOI5NVvE0uG7Xw/qUk7vt
ayWS382Mc/MxMoTHA6MT8w464ANSiqcl7OmqQ2i6ZdyQOm5r1Wi8qM8/KwLh88DopHzDnriO
LUbqT7du0a+j+zZ8rc8H+l4z/q8SHGcD7+z7w6c4ANCisuTVbxNLhu18P6lJO77Wslkt/NjH
PzMTKExwOjE/MOOuLEl7OmqQ2i6ZdyQOm5r1Wi8qM8/KwLh88DopHzDnrgAy/Hf/ACTzxL/2
Crr/ANFNWZV/xANQ1zwt4m0uHSLqCeSynt7Zp5IdtyzI6qU2yEgZx98L94e+MPzdd/6FDV/+
/wDZ/wDx+gC7RVLzdd/6FDV/+/8AZ/8Ax+jzdd/6FDV/+/8AZ/8Ax+gC7RVLzdd/6FDV/wDv
/Z//AB+jzdd/6FDV/wDv/Z//AB+gCn4v/wCRK17/ALB1x/6LavTK8v1228Rap4e1PT4PCWqL
LdWksCF7izChmQqM4nPGTXqFABRRRQBz/iH/AJDnhP8A7Csn/pFdV0Fc/wCIf+Q54T/7Csn/
AKRXVdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUVxeq/FnwPouqXG
m3+vRpd2z7JUSCWQK3cbkUjI6EZ4OQeQa7CCeG6t4ri3ljmglQPHJGwZXUjIII4II5zQBJRR
RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVh+JbbV7m3gGmySGBXJu4
IJhBPNHjlY5CpAOMjA8sklSJY9p3blFAGfo19pd7pyDSJIDa2+IPJhXZ9nKgfumTgxsoIBQg
FehArQrL1LSGurhb6xvJLDUEQJ5yIrrKgJIjlQ/fQMSeCrDLBWXc2Y7HXM3kem6vHBp+qyZM
8QBknkIJCICQM4BJJIAALMQoJHL6hHqc3iTwrf380lukmpyLHpqiMrEv2O5IZ3AJaXrna2wZ
wA23ewB2lFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHP+If8AkOeE
/wDsKyf+kV1XQVz/AIh/5DnhP/sKyf8ApFdV0FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB
RRRQAUUUUAFFFFAHj+j/APJPPi1/2FdY/wDRQr0DwJ/yTzw1/wBgq1/9FLXn+j/8k8+LX/YV
FFFFABRRRQAUUVHPPDa28txcSxwwRIXkkkYKqKBkkk8AAc5oAkrn9buP7V8/Q7Ky+1zHaJ7h
jiGybh0Z2DK/mAYdVj+YHYS0YZXo/wCJj4j/AOf7R9MH+7Hc3YP5tDGVP+zLk/8ALLZ8+xY2
FnplnHZ2FpBaWsedkMEYjRckk4UcDJJP40Ac3Hat4Z1SbU9SSO/gdNkmtzbftVunHySKqBRA
CuSybQCwLJw8psa1PDdan4OuLeWOaCXU2eOSNgyupsrkggjggjnNdJXH6vpKWPizwzc21zPH
BNqsrPZDb5PmtaXJaUDbuVj3AYKSWYruYsQDsKKKKACiiigAooooAKKKKACiiigAooooAKKK
KACiiigAooooAKKKKAOf8Q/8hzwn/wBhWT/0iuq6Cuf8Q/8AIc8J/wDYVk/9IrqugoAKKKKA
CiiigAooooAKKKKACiiigAooooAKKKKACiiigAoorh/iJ4/uPAn9kmLRP7Qj1CVoTM92LeOF
xt2hnZSo3ZY8kYCE9AcAHL6P/wAk8+LX/YV1j/0UK9A8Cf8AJPPDX/YKtf8A0Utcf4e8OeIv
+FeeOrbUdJ+xanrd3qFzb2f2mOT/AF0QCrvB2/eyMnHTPFU/DvjzXPDV/wCFfBGu+D5LaeW3
AFFFFABRRRQAUUUUAFY+vaAmtfY5RP5N1ZS+fbM8SzRiQcgtG4wcED5lKuBkK67jnYooAy9N
1drq4axvrOSw1BEL+S7q6yoCAZInH30DEDkKwypZV3LnUqnqWlWOr26wX9tHOiOJIy3DROAQ
OeE/+wrJ/wCkV1Vi+1h3vJNL0lPP1EYEkjxMYLUEA7pGGAWAKkRBt7bl+6pLrh6hokNn4k8K
RQAUUUUAFFFFABRRRQAUUUUAc/4h/wCQ54T/AOwrJ/6RXVdBXP8AiH/kOeE/+wrJ/wCkV1XQ
UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVT1XSrHXNLuNM1O2jubO4TZLE/R
h/MEHBBHIIBGCKuUUAeP2fjbVPhjZnwp4l06fUri3iWPQLmyiwNSQEIkJAzskXKg9eP7x2mT
rPAXhKbSUuvEGuwxt4p1Z2lvpBIZBCpbKwRk52oqhRgE8r1IVcdpRQAUUUUAFFFFABRRRQAU
UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFZ+p6xb6X5UbpPcXU+fItbaIySSEYzwOFX
JUF2KopZdzDIoAuTzw2tvLcXEscMESF5JJGCqigZJJPAAHOaw/Nv/Ef/AB7NPp2kH5ZHlgkh
u7kdwm4q0C/w7iu9ssV2YSRpINEmvriK98Q/ZLueFxJa20cZMFowOQw3ZLyg8eaQvA+VU3Pu
3KAOf/saXQf3/h9NtonL6MgRIWH8Rh4HlydCBkRsc5Cs5kFPUNSh1PU/CskayRSR6xJHPbyg
CSCQWVySjgEjOCCCCQQQykqQT1lcv4hsLP8A4SzwnqP2SD7d/aEkH2nyx5nl/ZLptm7rtzzj
pmgDqKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOf8Q/8hzwn/wBh
WT/0iuq6Cuf8Q/8AIc8J/wDYVk/9IrqugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA
CsPWNY1K11mx0vS9PtLue5t57hmurxoFRYmiXAKxuSSZh2HQ1uVz+s+F7PXvEOm3mpWNje2N
raXMRhuohJ+8keEqwVgRwI3GevI9TQBJpviJZ5Ws9RijtNQS9NiYopGmjeXyBcAI+1SR5RyS
yryCOeCY5/F2nQX8ERbNrJFds04DZEkE8UBjVNuXZnkKgDklQAG3Cs+DwteaTpOmWmmRWJXR
tQkuLGBnMKSwvHKgR2CMUZROfmw5fywzEFzty7jwLqt/Y2ouZLSKeN72d44LmQAvLqEN2iLK
sCRtgKCRgQrnKqRhHP8AA2Of0/wbrK6zJeald2l0G0y5sZDczT3SzySNEfNaFyEjRtjboY8B
eAGYEbI5vB2tXVrE0smz7NdpcwWH9uXcm5hFLG7fbComjyJV+UKV/df9NGIAO4sb631Gzjur
WTzIXyASpUggkMrKcFWBBBUgEEEEAirFZfh7Szo+iQ2bJGjh5JXVJZJQGkdnPzyEu5yxyxxu
OThc7RqUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVT1LSrHV7dYL+2jnRHEk
ZbhonAIDow5Rxk4ZSCOxFXKKAOf/ALSvPD/ya7N9psT/AKvUorcjywO1wFyF4G4zALH97Ijw
u/oKK5/+ybnQP3nh6GAWA+aXSFjCKfU27ZCxMepUgozAf6ss7kA6Cuf8Q/8AIc8J/wDYVk/9
RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBz/iH/kOeE/+wrJ/6RXV
dBXP+If+Q54T/wCwrJ/6RXVdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR
RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA
Zeo6JDeXK6hbbLXVokCRXqxgttBJ8t+heIknKEjrkFWCsOX13X10/WPDEXiBo7GS21N2kvGV
o7SRPslwqusrfKhJYDy2bcDkDcuHbvKKAOf/AOE78H/9DXof/gxh/wDiqP8AhO/B/wD0Neh/
+DGH/wCKroKKAOf/AOE78H/9DXof/gxh/wDiqP8AhO/B/wD0Neh/+DGH/wCKroKKAOf/AOE7
8H/9DXof/gxh/wDiqP8AhO/B/wD0Neh/+DGH/wCKroKKAOf/AOE78H/9DXof/gxh/wDiqP8A
hO/B/wD0Neh/+DGH/wCKroKKAOf/AOE78H/9DXof/gxh/wDiqP8AhO/B/wD0Neh/+DGH/wCK
roKKAOf/AOE78H/9DXof/gxh/wDiqP8AhO/B/wD0Neh/+DGH/wCKroKKAOf/AOE78H/9DXof
/gxh/wDiqP8AhO/B/wD0Neh/+DGH/wCKroKKAOf/AOE78H/9DXof/gxh/wDiqP8AhO/B/wD0
Neh/+DGH/wCKroKKAOf/AOE78H/9DXof/gxh/wDiqP8AhO/B/wD0Neh/+DGH/wCKroKKAOf/
AOE78H/9DXof/gxh/wDiqP8AhO/B/wD0Neh/+DGH/wCKroKKAOL1DxLoOs+JPCtvpet6bfTr
qcjtHa3SSsF+x3IyQpJxkgZ9xXaUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR
QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF
FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR
QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF
FFFABRRRQAUUUUAFFFFABRRRQB//2Q==
--------------31626C68DD1F5865F8022D97--

--------------5A847703274CF428D025BE6D--

############################################################################
This message was posted through the fuzzy mailing list.
(1)  To subscribe to this mailing list, send a message body of
"SUB FUZZY-MAIL myFirstName mySurname" to listproc@dbai.tuwien.ac.at
(2)  To unsubscribe from this mailing list, send a message body of
"UNSUB FUZZY-MAIL" or "UNSUB FUZZY-MAIL yoursubscription@email.address.com"
to listproc@dbai.tuwien.ac.at
(3)  To reach the human who maintains the list, send mail to
fuzzy-owner@dbai.tuwien.ac.at
(4)  WWW access and other information on Fuzzy Sets and Logic see
http://www.dbai.tuwien.ac.at/ftp/mlowner/fuzzy-mail.info
(5) WWW archive: http://www.dbai.tuwien.ac.at/marchives/fuzzy-mail/index.html
```

This archive was generated by hypermail 2b25 : Wed Oct 11 2000 - 05:12:47 MET DST