Fuzzy logic

fuzzy logic:

Fuzzy logic is a superset of conventional (Boolean) logic that has been
extended to handle the concept of partial truth -- truth values between
"completely true" and "completely false". It was introduced by Dr.
Lotfi
Zadeh of UC/Berkeley in the 1960's as a means to model the uncertainty
of natural language.
Zadeh says that rather than regarding fuzzy theory as a single theory,
we
should regard the process of ``fuzzification'' as a methodology to
generalize ANY specific theory from a crisp (discrete) to a continuous
(fuzzy) form.

Fuzzy Subsets:

Just as there is a strong relationship between Boolean logic and the
concept of a subset, there is a similar strong relationship between
fuzzy
logic and fuzzy subset theory.

In classical set theory, a subset U of a set S can be defined as a
mapping from the elements of S to the elements of the set {0, 1},

U: S --> {0, 1}

This mapping may be represented as a set of ordered pairs, with exactly
one ordered pair present for each element of S. The first element of
the
ordered pair is an element of the set S, and the second element is an
element of the set {0, 1}. The value zero is used to represent
non-membership, and the value one is used to represent membership. The
truth or falsity of the statement

x is in U

is determined by finding the ordered pair whose first element is x.
The
statement is true if the second element of the ordered pair is 1, and
the
statement is false if it is 0.

Similarly, a fuzzy subset F of a set S can be defined as a set of
ordered
pairs, each with the first element from S, and the second element from
the interval [0,1], with exactly one ordered pair present for each
element of S. This defines a mapping between elements of the set S and
values in the interval [0,1]. The value zero is used to represent

complete non-membership, the value one is used to represent complete
membership, and values in between are used to represent intermediate
DEGREES OF MEMBERSHIP. The set S is referred to as the UNIVERSE OF
DISCOURSE for the fuzzy subset F. Frequently, the mapping is described
as a function, the MEMBERSHIP FUNCTION of F. The degree to which the
statement

x is in F

is true is determined by finding the ordered pair whose first element
is
x. The DEGREE OF TRUTH of the statement is the second element of the
ordered pair.

In practice, the terms "membership function" and fuzzy subset get used
interchangeably.

That's a lot of mathematical baggage, so here's an example. Let's
talk about people and "tallness". In this case the set S (the
universe of discourse) is the set of people. Let's define a fuzzy
subset TALL, which will answer the question "to what degree is person
x tall?" Zadeh describes TALL as a LINGUISTIC VARIABLE, which
represents our cognitive category of "tallness". To each person in the
universe of discourse, we have to assign a degree of membership in the
fuzzy subset TALL. The easiest way to do this is with a membership
function based on the person's height.

tall(x) = { 0, if height(x) < 5 ft.,
(height(x)-5ft.)/2ft., if 5 ft. <= height (x) <= 7 ft.,
1, if height(x) > 7 ft. }

A graph of this looks like:

1.0 + +-------------------
| /
| /
0.5 + /
| /
| /
0.0 +-------------+-----+-------------------
| |
5.0 7.0

height, ft. ->

Given this definition, here are some example values:

Person Height degree of tallness

--------------------------------------
Billy 3' 2" 0.00 [I think]
Yoke 5' 5" 0.21
Drew 5' 9" 0.38
Erik 5' 10" 0.42
Mark 6' 1" 0.54
Kareem 7' 2" 1.00 [depends on who you ask]

Expressions like "A is X" can be interpreted as degrees of truth,
e.g., "Drew is TALL" = 0.38.

Note: Membership functions used in most applications almost never have
as
simple a shape as tall(x). At minimum, they tend to be triangles
pointing
up, and they can be much more complex than that. Also, the discussion
characterizes membership functions as if they always are based on a
single criterion, but this isn't always the case, although it is quite
common. One could, for example, want to have the membership function
for
TALL depend on both a person's height and their age (he's tall for his
age). This is perfectly legitimate, and occasionally used in practice.
It's referred to as a two-dimensional membership function, or a "fuzzy
relation". It's also possible to have even more criteria, or to have
the
membership function depend on elements from two completely different
universes of discourse.

Logic Operations:->

Now that we know what a statement like "X is LOW" means in fuzzy logic,
how do we interpret a statement like

X is LOW and Y is HIGH or (not Z is MEDIUM)

The standard definitions in fuzzy logic are:

truth (not x) = 1.0 - truth (x)
truth (x and y) = minimum (truth(x), truth(y))
truth (x or y) = maximum (truth(x), truth(y))

Some researchers in fuzzy logic have explored the use of other
interpretations of the AND and OR operations, but the definition for
the
NOT operation seems to be safe.

Note that if you plug just the values zero and one into these
definitions, you get the same truth tables as you would expect from

conventional Boolean logic. This is known as the EXTENSION PRINCIPLE,
which states that the classical results of Boolean logic are recovered
from fuzzy logic operations when all fuzzy membership grades are
restricted to the traditional set {0, 1}. This effectively establishes
fuzzy subsets and logic as a true generalization of classical set
theory
and logic. In fact, by this reasoning all crisp (traditional) subsets
ARE
fuzzy subsets of this very special type; and there is no conflict
between
fuzzy and crisp methods.

Some examples:->

assume the same definition of TALL as above, and in addition,
assume that we have a fuzzy subset OLD defined by the membership
function:

old (x) = { 0, if age(x) < 18 yr.
(age(x)-18 yr.)/42 yr., if 18 yr. <= age(x) <= 60 yr.
1, if age(x) > 60 yr. }

And for compactness, let

a = X is TALL and X is OLD
b = X is TALL or X is OLD
c = not (X is TALL)

Then we can compute the following values.

height age X is TALL X is OLD a b c
----------------------------------------------------------

used of fuzzy logic :->

Fuzzy logic is used directly in very few applications. The Sony PalmTop
apparently uses a fuzzy logic decision tree algorithm to perform
handwritten (well, computer lightpen) Kanji character recognition.

A fuzzy expert system:->
A fuzzy expert system is an expert system that uses a collection of
fuzzy membership functions and rules, instead of Boolean logic, to
reason about data. The rules in a fuzzy expert system are usually of a
form similar to the following:

if x is low and y is high then z = medium

where x and y are input variables (names for know data values), z is an
output variable (a name for a data value to be computed), low is a
membership function (fuzzy subset) defined on x, high is a membership
function defined on y, and medium is a membership function defined on
z.
The antecedent (the rule's premise) describes to what degree the rule
applies, while the conclusion (the rule's consequent) assigns a
membership function to each of one or more output variables. Most
tools
for working with fuzzy expert systems allow more than one conclusion
per
rule. The set of rules in a fuzzy expert system is known as the
rulebase
or knowledge base.

The general inference process proceeds in three (or four) steps.

1. Under FUZZIFICATION, the membership functions defined on the
input variables are applied to their actual values, to determine the
degree of truth for each rule premise.

2. Under INFERENCE, the truth value for the premise of each rule is
computed, and applied to the conclusion part of each rule. This
results
in one fuzzy subset to be assigned to each output variable for each
rule. Usually only MIN or PRODUCT are used as inference rules. In
MIN
inferencing, the output membership function is clipped off at a
height
corresponding to the rule premise's computed degree of truth (fuzzy
logic AND). In PRODUCT inferencing, the output membership function
is
scaled by the rule premise's computed degree of truth.

3. Under COMPOSITION, all of the fuzzy subsets assigned to each output
variable are combined together to form a single fuzzy subset
for each output variable. Again, usually MAX or SUM are used. In
MAX
composition, the combined output fuzzy subset is constructed by
taking
the pointwise maximum over all of the fuzzy subsets assigned
tovariable
by the inference rule (fuzzy logic OR). In SUM composition, the
combined output fuzzy subset is constructed by taking the pointwise
sum
over all of the fuzzy subsets assigned to the output variable by the
inference rule.

4. Finally is the (optional) DEFUZZIFICATION, which is used when it is
useful to convert the fuzzy output set to a crisp number. There are
more defuzzification methods than you can shake a stick at (at least
30). Two of the more common techniques are the CENTROID and MAXIMUM
methods. In the CENTROID method, the crisp value of the output
variable
is computed by finding the variable value of the center of gravity
of
the membership function for the fuzzy value. In the MAXIMUM method,
one
of the variable values at which the fuzzy subset has its maximum
truth
value is chosen as the crisp value for the output variable.

Extended Example:

Assume that the variables x, y, and z all take on values in the
interval
[0,10], and that the following membership functions and rules are
defined:

low(t) = 1 - ( t / 10 )
high(t) = t / 10

rule 1: if x is low and y is low then z is high
rule 2: if x is low and y is high then z is low
rule 3: if x is high and y is low then z is low
rule 4: if x is high and y is high then z is high

Notice that instead of assigning a single value to the output variable
z, each
rule assigns an entire fuzzy subset (low or high).

Notes:

1. In this example, low(t)+high(t)=1.0 for all t. This is not
required, but
it is fairly common.

2. The value of t at which low(t) is maximum is the same as the value
of t at
which high(t) is minimum, and vice-versa. This is also not
required, but
fairly common.

3. The same membership functions are used for all variables. This
isn't
required, and is also *not* common.

In the fuzzification subprocess, the membership functions defined on
the
input variables are applied to their actual values, to determine the
degree of truth for each rule premise. The degree of truth for a
rule's
premise is sometimes referred to as its ALPHA. If a rule's premise has
a
nonzero degree of truth (if the rule applies at all...) then the rule
is
said to FIRE. For example,

x y low(x) high(x) low(y) high(y) alpha1 alpha2 alpha3
alpha4
------------------------------------------------------------------------------
0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0
0.0
0.0 3.2 1.0 0.0 0.68 0.32 0.68 0.32 0.0
0.0
0.0 6.1 1.0 0.0 0.39 0.61 0.39 0.61 0.0
0.0
0.0 10.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0
0.0
3.2 0.0 0.68 0.32 1.0 0.0 0.68 0.0 0.32
0.0
6.1 0.0 0.39 0.61 1.0 0.0 0.39 0.0 0.61
0.0
10.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0
0.0
3.2 3.1 0.68 0.32 0.69 0.31 0.68 0.31 0.32
0.31
3.2 3.3 0.68 0.32 0.67 0.33 0.67 0.33 0.32
0.32
10.0 10.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0
1.0

In the inference subprocess, the truth value for the premise of each
rule is
computed, and applied to the conclusion part of each rule. This
results in
one fuzzy subset to be assigned to each output variable for each rule.

MIN and PRODUCT are two INFERENCE METHODS or INFERENCE RULES. In
MIN
inferencing, the output membership function is clipped off at a height
corresponding to the rule premise's computed degree of truth. This

corresponds to the traditional interpretation of the fuzzy logic AND
operation. In PRODUCT inferencing, the output membership function is
scaled by the rule premise's computed degree of truth.

For example, let's look at rule 1 for x = 0.0 and y = 3.2. As shown in
the
table above, the premise degree of truth works out to 0.68. For this
rule,
MIN inferencing will assign z the fuzzy subset defined by the
membership
function:

rule1(z) = { z / 10, if z <= 6.8
0.68, if z >= 6.8 }

For the same conditions, PRODUCT inferencing will assign z the fuzzy
subset
defined by the membership function:

rule1(z) = 0.68 * high(z)
= 0.068 * z

Note: The terminology used here is slightly nonstandard. In most
texts,
the term "inference method" is used to mean the combination of the
things
referred to separately here as "inference" and "composition." Thus
you'll see such terms as "MAX-MIN inference" and "SUM-PRODUCT
inference"
in the literature. They are the combination of MAX composition and MIN
inference, or SUM composition and PRODUCT inference, respectively.
You'll also see the reverse terms "MIN-MAX" and "PRODUCT-SUM" -- these
mean the same things as the reverse order. It seems clearer to
describe
the two processes separately.

In the composition subprocess, all of the fuzzy subsets assigned to
each
output variable are combined together to form a single fuzzy subset for
each
output variable.

MAX composition and SUM composition are two COMPOSITION RULES. In MAX
composition, the combined output fuzzy subset is constructed by taking
the pointwise maximum over all of the fuzzy subsets assigned to the
output variable by the inference rule. In SUM composition, the
combined

output fuzzy subset is constructed by taking the pointwise sum over all
of the fuzzy subsets assigned to the output variable by the inference
rule. Note that this can result in truth values greater than one! For
this reason, SUM composition is only used when it will be followed by a
defuzzification method, such as the CENTROID method, that doesn't have
a
problem with this odd case. Otherwise SUM composition can be combined
with normalization and is therefore a general purpose method again.

For example, assume x = 0.0 and y = 3.2. MIN inferencing would assign
the
following four fuzzy subsets to z:

rule1(z) = { z / 10, if z <= 6.8
0.68, if z >= 6.8 }

rule2(z) = { 0.32, if z <= 6.8
1 - z / 10, if z >= 6.8 }

rule3(z) = 0.0

rule4(z) = 0.0

MAX composition would result in the fuzzy subset:

fuzzy(z) = { 0.32, if z <= 3.2
z / 10, if 3.2 <= z <= 6.8
0.68, if z >= 6.8 }

PRODUCT inferencing would assign the following four fuzzy subsets to z:

rule1(z) = 0.068 * z
rule2(z) = 0.32 - 0.032 * z
rule3(z) = 0.0
rule4(z) = 0.0

SUM composition would result in the fuzzy subset:

fuzzy(z) = 0.32 + 0.036 * z

Sometimes it is useful to just examine the fuzzy subsets that are the
result of the composition process, but more often, this FUZZY VALUE
needs
to be converted to a single number -- a CRISP VALUE. This is what the
defuzzification subprocess does.

fuzzy numbers and fuzzy arithmetic:->

Fuzzy numbers are fuzzy subsets of the real line. They have a peak or
plateau with membership grade 1, over which the members of the
universe are completely in the set. The membership function is
increasing towards the peak and decreasing away from it.

Fuzzy numbers are used very widely in fuzzy control applications. A
typical
case is the triangular fuzzy number

1.0 + +
| /
| /
0.5 + /
| /
| /
0.0 +-------------+-----+-----+--------------
| | |
5.0 7.0 9.0

which is one form of the fuzzy number 7. Slope and trapezoidal
functions
are also used, as are exponential curves similar to Gaussian
probability
densities.

There are more defuzzification methods than you can shake a stick at.
A
couple of years ago, Mizumoto did a short paper that compared about ten
defuzzification methods. Two of the more common techniques are the
CENTROID and MAXIMUM methods. In the CENTROID method, the crisp value
of
the output variable is computed by finding the variable value of the
center of gravity of the membership function for the fuzzy value. In
the
MAXIMUM method, one of the variable values at which the fuzzy subset
has
its maximum truth value is chosen as the crisp value for the output
variable. There are several variations of the MAXIMUM method that
differ
only in what they do when there is more than one variable value at
which
this maximum truth value occurs. One of these, the AVERAGE-OF-MAXIMA
method, returns the average of the variable values at which the maximum
truth value occurs.

For example, go back to our previous examples. Using MAX-MIN
inferencing
and AVERAGE-OF-MAXIMA defuzzification results in a crisp value of 8.4
for
z. Using PRODUCT-SUM inferencing and CENTROID defuzzification results
in
a crisp value of 5.6 for z, as follows.

Earlier on in the FAQ, we state that all variables (including z) take
on
values in the range [0, 10]. To compute the centroid of the function
f(x),
you divide the moment of the function by the area of the function. To
compute
the moment of f(x), you compute the integral of x*f(x) dx, and to
compute the
area of f(x), you compute the integral of f(x) dx. In this case, we
would
compute the area as integral from 0 to 10 of (0.32+0.036*z) dz, which
is

(0.32 * 10 + 0.018*100) =
(3.2 + 1.8) =
5.0

and the moment as the integral from 0 to 10 of (0.32*z+0.036*z*z) dz,
which is

(0.16 * 10 * 10 + 0.012 * 10 * 10 * 10) =
(16 + 12) =
28

Finally, the centroid is 28/5 or 5.6.

Note: Sometimes the composition and defuzzification processes are
combined, taking advantage of mathematical relationships that simplify
the process of computing the final output variable values.

The Mizumoto reference is probably "Improvement Methods of Fuzzy
Controls", in Proceedings of the 3rd IFSA Congress, pages 60-62, 1989.

=====================================================
===========

Used of fuzzy expert systems :->

To date, fuzzy expert systems are the most common use of fuzzy logic.
They

are used in several wide-ranging fields, including:
o Linear and Nonlinear Control
o Pattern Recognition
o Financial Systems
o Operation Research
o Data Analysis

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Fuzzy logic

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