The point of fuzzy logic is to map an input space to an output
space, and the primary mechanism for doing this is a list of if-then
statements called rules. All rules are evaluated in parallel, and
the order of the rules is unimportant. The rules themselves are useful
because they refer to variables and the adjectives that describe those
variables. Before you can build a system that interprets rules, you
must define all the terms you plan on using and the adjectives that
describe them. To say that the water is hot, you need to define the
range that the water's temperature can be expected to vary as well
as what we mean by the word *hot*. The following
diagram provides a roadmap for the fuzzy inference process. It shows
the general description of a fuzzy system on the left and a specific
fuzzy system on the right.

To summarize the concept of fuzzy inference depicted in this
figure, *fuzzy inference is a method that interprets the
values in the input vector and, based on some set of rules, assigns
values to the output vector.*

This topic guides you through the fuzzy logic process step by step by providing an introduction to the theory and practice of fuzzy logic.

Fuzzy logic starts with the concept of a fuzzy set. A *fuzzy
set* is a set without a crisp, clearly defined boundary.
It can contain elements with only a partial degree of membership.

To understand what a fuzzy set is, first consider the definition
of a *classical set*. A classical set is a container
that wholly includes or wholly excludes any given element. For example,
the set of days of the week unquestionably includes Monday, Thursday,
and Saturday. It just as unquestionably excludes butter, liberty,
and dorsal fins, and so on.

This type of set is called a classical set because it has been around for a long time. It was Aristotle who first formulated the Law of the Excluded Middle, which says X must either be in set A or in set not-A. Another version of this law is:

Of any subject, one thing must be either asserted
or denied. |

To restate this law with annotations: "Of any subject (say Monday), one thing (a day of the week) must be either asserted or denied (I assert that Monday is a day of the week)." This law demands that opposites, the two categories A and not-A, should between them contain the entire universe. Everything falls into either one group or the other. There is no thing that is both a day of the week and not a day of the week.

Now, consider the set of days comprising a weekend. The following diagram attempts to classify the weekend days.

Most would agree that Saturday and Sunday belong, but what about Friday? It feels like a part of the weekend, but somehow it seems like it should be technically excluded. Thus, in the preceding diagram, Friday tries its best to "straddle on the fence." Classical or normal sets would not tolerate this kind of classification. Either something is in or it is out. Human experience suggests something different, however, straddling the fence is part of life.

Of course individual perceptions and cultural background must
be taken into account when you define what constitutes the weekend.
Even the dictionary is imprecise, defining the weekend as the period
from Friday night or Saturday to Monday morning. You are entering
the realm where sharp-edged, yes-no logic stops being helpful. Fuzzy
reasoning becomes valuable exactly when you work with how people really
perceive the concept *weekend* as opposed to a
simple-minded classification useful for accounting purposes only.
More than anything else, the following statement lays the foundations
for fuzzy logic.

In fuzzy logic, the truth of any statement becomes
a matter of degree. |

Any statement can be fuzzy. The major advantage that fuzzy reasoning offers is the ability to reply to a yes-no question with a not-quite-yes-or-no answer. Humans do this kind of thing all the time (think how rarely you get a straight answer to a seemingly simple question), but it is a rather new trick for computers.

How does it work? Reasoning in fuzzy logic is just a matter of generalizing the familiar yes-no (Boolean) logic. If you give true the numerical value of 1 and false the numerical value of 0, this value indicates that fuzzy logic also permits in-between values like 0.2 and 0.7453. For instance:

Q: Is Saturday a weekend day? |

A: 1 (yes, or true) |

Q: Is Tuesday a weekend day? |

A: 0 (no, or false) |

Q: Is Friday a weekend day? |

A: 0.8 (for the most part yes, but not completely) |

Q: Is Sunday a weekend day? |

A: 0.95 (yes, but not quite as much as Saturday). |

The following plot on the left shows the truth values for weekend-ness if you are forced to respond with an absolute yes or no response. On the right, is a plot that shows the truth value for weekend-ness if you are allowed to respond with fuzzy in-between values.

Technically, the representation on the right is from the domain
of *multivalued logic* (or multivalent logic).
If you ask the question "Is X a member of set A?" the
answer might be yes, no, or any one of a thousand intermediate values
in between. Thus, X might have partial membership in A. Multivalued
logic stands in direct contrast to the more familiar concept of two-valued
(or bivalent yes-no) logic.

To return to the example, now consider a continuous scale time plot of weekend-ness shown in the following plots.

By making the plot continuous, you are defining the degree to which any given instant belongs in the weekend rather than an entire day. In the plot on the left, notice that at midnight on Friday, just as the second hand sweeps past 12, the weekend-ness truth value jumps discontinuously from 0 to 1. This is one way to define the weekend, and while it may be useful to an accountant, it may not really connect with your own real-world experience of weekend-ness.

The plot on the right shows a smoothly varying curve that accounts
for the fact that all of Friday, and, to a small degree, parts of
Thursday, partake of the quality of weekend-ness and thus deserve
partial membership in the fuzzy set of weekend moments. The curve
that defines the weekend-ness of any instant in time is a function
that maps the input space (time of the week) to the output space (weekend-ness).
Specifically it is known as a *membership function*. See Membership Functions for a more detailed
discussion.

As another example of fuzzy sets, consider the question of seasons. What season is it right now? In the northern hemisphere, summer officially begins at the exact moment in the earth's orbit when the North Pole is pointed most directly toward the sun. It occurs exactly once a year, in late June. Using the astronomical definitions for the season, you get sharp boundaries as shown on the left in the figure that follows. But what you experience as the seasons vary more or less continuously as shown on the right in the following figure (in temperate northern hemisphere climates).

A *membership function* (MF) is a curve that defines
how each point in the input space is mapped to a membership value
(or degree of membership) between 0 and 1. The input space is sometimes
referred to as the *universe of discourse*, a fancy
name for a simple concept.

One of the most commonly used examples of a fuzzy set is the set of tall people. In this case, the universe of discourse is all potential heights, say from 3 feet to 9 feet, and the word tall would correspond to a curve that defines the degree to which any person is tall. If the set of tall people is given the well-defined (crisp) boundary of a classical set, you might say all people taller than 6 feet are officially considered tall. However, such a distinction is clearly absurd. It may make sense to consider the set of all real numbers greater than 6 because numbers belong on an abstract plane, but when we want to talk about real people, it is unreasonable to call one person short and another one tall when they differ in height by the width of a hair.

If the kind of distinction shown previously is unworkable, then
what is the right way to define the set of tall people? Much as with
the plot of weekend days, the figure following shows a smoothly varying
curve that passes from not-tall to tall. The output-axis is a number
known as the membership value between 0 and 1. The curve is known
as a *membership function* and is often given the
designation of µ. This curve defines the transition from not
tall to tall. Both people are tall to some degree, but one is significantly
less tall than the other.

Subjective interpretations and appropriate units are built right into fuzzy sets. If you say "She's tall," the membership function tall should already take into account whether you are referring to a six-year-old or a grown woman. Similarly, the units are included in the curve. Certainly it makes no sense to say "Is she tall in inches or in meters?"

The only condition a membership function must really satisfy is that it must vary between 0 and 1. The function itself can be an arbitrary curve whose shape we can define as a function that suits us from the point of view of simplicity, convenience, speed, and efficiency.

A classical set might be expressed as

A = {x | x >
6} |

A fuzzy set is an extension of a classical set. If X is the
universe of discourse and its elements are denoted by *x*,
then a fuzzy set A in X is defined as a set of ordered pairs.

A = {x, µ_{A}(x) | x ∈
X} |

µ_{A}*(x) *is called
the membership function (or MF) of *x* in A. The
membership function maps each element of X to a membership value between
0 and 1.

The toolbox includes 11 built-in membership function types. These 11 functions are, in turn, built from several basic functions:

piece-wise linear functions

the Gaussian distribution function

the sigmoid curve

quadratic and cubic polynomial curves

For detailed information on any of the membership functions
mentioned next, see the corresponding reference page. By convention,
all membership functions have the letters `mf`

at
the end of their names.

The simplest membership functions are formed using straight
lines. Of these, the simplest is the *triangular* membership
function, and it has the function name `trimf`

. This
function is nothing more than a collection of three points forming
a triangle. The *trapezoidal* membership function, `trapmf`

,
has a flat top and really is just a truncated triangle curve. These
straight line membership functions have the advantage of simplicity.

Two membership functions are built on the *Gaussian* distribution
curve: a simple Gaussian curve and a two-sided composite of two different
Gaussian curves. The two functions are `gaussmf`

and `gauss2mf`

.

The *generalized bell* membership function
is specified by three parameters and has the function name `gbellmf`

.
The bell membership function has one more parameter than the Gaussian
membership function, so it can approach a non-fuzzy set if the free
parameter is tuned. Because of their smoothness and concise notation,
Gaussian and bell membership functions are popular methods for specifying
fuzzy sets. Both of these curves have the advantage of being smooth
and nonzero at all points.

Although the Gaussian membership functions
and bell membership functions achieve smoothness, they are unable
to specify asymmetric membership functions, which are important in
certain applications. Next, you define the *sigmoidal* membership function, which is either open
left or right. Asymmetric and closed (i.e. not open to the left or
right) membership functions can be synthesized using two sigmoidal
functions, so in addition to the basic `sigmf`

, you also have the difference
between two sigmoidal functions, `dsigmf`

, and the product of two sigmoidal
functions `psigmf`

.

Polynomial based curves account for several of the membership
functions in the toolbox. Three related membership functions are the *Z, S*,
and** ***Pi curves*,
all named because of their shape. The function `zmf`

is the asymmetrical polynomial
curve open to the left, `smf`

is the mirror-image function that
opens to the right, and `pimf`

is zero on both extremes with
a rise in the middle.

There is a very wide selection to choose from when you're selecting a membership function. You can also create your own membership functions with the toolbox. However, if a list based on expanded membership functions seems too complicated, just remember that you could probably get along very well with just one or two types of membership functions, for example the triangle and trapezoid functions. The selection is wide for those who want to explore the possibilities, but expansive membership functions are not necessary for good fuzzy inference systems. Finally, remember that more details are available on all these functions in the reference section.

Fuzzy sets describe vague concepts (e.g., fast runner, hot weather, weekend days).

A fuzzy set admits the possibility of partial membership in it. (e.g., Friday is sort of a weekend day, the weather is rather hot).

The degree an object belongs to a fuzzy set is denoted by a membership value between 0 and 1. (e.g., Friday is a weekend day to the degree 0.8).

A membership function associated with a given fuzzy set maps an input value to its appropriate membership value.

Now that you understand the fuzzy inference, you need to see how fuzzy inference connects with logical operations.

The most important thing to realize about fuzzy logical reasoning is the fact that it is a superset of standard Boolean logic. In other words, if you keep the fuzzy values at their extremes of 1 (completely true), and 0 (completely false), standard logical operations will hold. As an example, consider the following standard truth tables.

Now, because in fuzzy logic the truth of any statement is a matter of degree, can these truth tables be altered? The input values can be real numbers between 0 and 1. What function preserves the results of the AND truth table (for example) and also extend to all real numbers between 0 and 1?

One answer is the *min* operation. That is,
resolve the statement *A* AND *B*,
where *A* and *B* are limited
to the range (0,1), by using the function *min*(*A*,*B*).
Using the same reasoning, you can replace the OR operation with the *max* function,
so that *A* OR *B* becomes equivalent
to *max*(*A,B*). Finally, the
operation NOT *A* becomes equivalent to the operation $$1-A$$.
Notice how the previous truth table is completely unchanged by this
substitution.

Moreover, because there is a function behind the truth table rather than just the truth table itself, you can now consider values other than 1 and 0.

The next figure uses a graph to show the same information. In
this figure, the truth table is converted to a plot of two fuzzy sets
applied together to create one fuzzy set. The upper part of the figure
displays plots corresponding to the preceding two-valued truth tables,
while the lower part of the figure displays how the operations work
over a continuously varying range of truth values *A* and *B* according
to the fuzzy operations you have defined.

Given these three functions, you can resolve any construction using fuzzy sets and the fuzzy logical operation AND, OR, and NOT.

In this case, you defined only one particular correspondence between two-valued and multivalued logical operations for AND, OR, and NOT. This correspondence is by no means unique.

In more general terms, you are defining what are known as the
fuzzy intersection or conjunction (AND), fuzzy union or disjunction
(OR), and fuzzy complement (NOT). The classical operators for these
functions are: AND = *min*, OR = *max*,
and NOT = additive complement. Typically, most fuzzy logic applications
make use of these operations and leave it at that. In general, however,
these functions are arbitrary to a surprising degree. Fuzzy Logic Toolbox™ software
uses the classical operator for the fuzzy complement as shown in the
previous figure, but also enables you to customize the AND and OR
operators.

The intersection of two fuzzy sets* A* and *B* is
specified in general by a binary mapping *T*, which
aggregates two membership functions as follows:

µ_{A∩B}(*x*) = *T*(µ_{A}(*x*), µ_{B}(*x*))

For example, the binary operator *T* may
represent the multiplication of $${\mu}_{A}\left(x\right)\text{and}{\mu}_{B}\left(x\right)$$. These fuzzy intersection
operators, which are usually referred to as *T-*norm
(Triangular norm) operators, meet the following basic requirements:

A *T-norm* operator is a binary mapping *T*(`.,.`

) satisfying

* boundary*: *T*(0, 0) = 0, *T*(*a*, 1) = *T*(1,* a) = a*

* monotonicity*: *T(a, b)* <= *T(c, d)* if* a <= c *and *b <= d
commutativity: T(a, b) = T(b, a)
associativity: T(a, T(b, c)) = T(T(a, b), c)*

The first requirement imposes the correct generalization to
crisp sets. The second requirement implies that a decrease in the
membership values in *A* or *B* cannot
produce an increase in the membership value in *A* intersection *B*.
The third requirement indicates that the operator is indifferent to
the order of the fuzzy sets to be combined. Finally, the fourth requirement
allows us to take the intersection of any number of sets in any order
of pair-wise groupings.

Like fuzzy intersection, the fuzzy union operator is specified
in general by a binary mapping *S*:

µ_{A∪B}(*x*) = S(µ_{A}(*x*), µ_{B}(*x*))

For example, the binary operator S can represent the addition
of $${\mu}_{A}\left(x\right)\text{and}{\mu}_{B}\left(x\right)$$. These fuzzy union operators,
which are often referred to as *T-conorm (or S-norm)* operators,
must satisfy the following basic requirements:

A *T**-conorm* (or *S**-norm*) operator is a binary mapping *S*(`.,.`

) satisfying

* boundary*: *S*(1, 1) = 1, *S*(*a, *0*) = S(*0*, a) = a*

* monotonicity*: *S(a, b)* <= S(c, d) if *a <= c *and *b <= d*

* commutativity*: *S(a, b) = S(b, a)*

* associativity*: *S(a, S(b, c)) = S(S(a, b), c)*

Several parameterized *T*-norms and dual *T*-conorms
have been proposed in the past, such as those of Yager[10], Dubois and Prade [1], Schweizer and Sklar [7], and Sugeno [8], found in the Bibliography.
Each of these provides a way to vary the gain on the function so that
it can be very restrictive or very permissive.

Fuzzy sets and fuzzy operators are the subjects and verbs of fuzzy logic. These if-then rule statements are used to formulate the conditional statements that comprise fuzzy logic.

A single fuzzy if-then rule assumes the form

if x is A then y is B |

where *A* and *B* are
linguistic values defined by fuzzy sets on the ranges (universes of
discourse) X and Y, respectively. The if-part of the rule "*x* is *A*"
is called the *antecedent* or premise,
while the then-part of the rule "*y* is *B*"
is called the *consequent* or conclusion. An example of such
a rule might be

If service is good then tip is average |

The concept *good* is represented as a number
between 0 and 1, and so the antecedent is an interpretation that returns
a single number between 0 and 1. Conversely, *average* is
represented as a fuzzy set, and so the consequent is an assignment
that assigns the entire fuzzy set B to the output variable *y*.
In the if-then rule, the word *is* gets used in
two entirely different ways depending on whether it appears in the
antecedent or the consequent. In MATLAB^{®} terms, this usage is
the distinction between a relational test using "=="
and a variable assignment using the "=" symbol. A less
confusing way of writing the rule would be

If service == good then tip = average |

In general, the input to an if-then rule is the current value
for the input variable (in this case, *service*)
and the output is an entire fuzzy set (in this case, *average*).
This set will later be *defuzzified, *assigning
one value to the output. The concept of defuzzification
is described in the next section.

Interpreting an if-then rule involves distinct parts: first
evaluating the antecedent (which involves *fuzzifying* the
input and applying any necessary *fuzzy operators*)
and second applying that result to the consequent (known as *implication*). In the case of two-valued or
binary logic, if-then rules do not present much difficulty. If the
premise is true, then the conclusion is true. If you relax the restrictions
of two-valued logic and let the antecedent be a fuzzy statement, how
does this reflect on the conclusion? The answer is a simple one. if
the antecedent is true to some degree of membership, then the consequent
is also true to that same degree.

Thus:

in binary logic: *p* → *q* (*p* and *q* are either both true or both false.)

in fuzzy logic: 0.5 *p* → 0.5 *q* (Partial antecedents provide partial implication.)

The antecedent of a rule can have multiple parts.

if sky is gray and wind is strong and barometer
is falling, then ... |

in which case all parts of the antecedent are calculated simultaneously and resolved to a single number using the logical operators described in the preceding section. The consequent of a rule can also have multiple parts.

if temperature is cold then hot water valve is open
and cold water valve is shut |

in which case all consequents are affected equally by the result
of the antecedent. How is the consequent affected by the antecedent?
The consequent specifies a fuzzy set be assigned to the output. The *implication
function* then modifies that fuzzy set to the degree specified
by the antecedent. The most common ways to modify the output fuzzy
set are truncation using the `min`

function (where
the fuzzy set is truncated as shown in the following figure) or scaling
using the `prod`

function (where the output fuzzy
set is squashed). Both are supported by the toolbox, but you use truncation
for the examples in this section.

Interpreting if-then rules is a three-part process. This process is explained in detail in the next section:

**Fuzzify inputs**: Resolve all fuzzy statements in the antecedent to a degree of membership between 0 and 1. If there is only one part to the antecedent, then this is the degree of support for the rule.**Apply fuzzy operator to multiple part antecedents**: If there are multiple parts to the antecedent, apply fuzzy logic operators and resolve the antecedent to a single number between 0 and 1. This is the degree of support for the rule.**Apply implication method**: Use the degree of support for the entire rule to shape the output fuzzy set. The consequent of a fuzzy rule assigns an entire fuzzy set to the output. This fuzzy set is represented by a membership function that is chosen to indicate the qualities of the consequent. If the antecedent is only partially true, (i.e., is assigned a value less than 1), then the output fuzzy set is truncated according to the implication method.

In general, one rule alone is not effective. Two or more rules
that can play off one another are needed. The output of each rule
is a fuzzy set. The output fuzzy sets for each rule are then aggregated
into a single output fuzzy set. Finally the resulting set is defuzzified,
or resolved to a single number. Build Mamdani Systems (GUI) shows how the whole process works
from beginning to end for a particular type of fuzzy inference system
called a *Mamdani type*.

[1] Dubois, D. and H. Prade, *Fuzzy
Sets and Systems: Theory and Applications*, Academic Press,
New York, 1980.

[2] Kaufmann, A. and M.M. Gupta, *Introduction
to Fuzzy Arithmetic*, V.N. Reinhold, 1985.

[3] Lee, C.-C., "Fuzzy logic in control
systems: fuzzy logic controller-parts 1 and 2," *IEEE
Transactions on Systems, Man, and Cybernetics*, Vol. 20,
No. 2, pp 404-435, 1990.

[4] Mamdani, E.H. and S. Assilian, "An experiment
in linguistic synthesis with a fuzzy logic controller," *International
Journal of Man-Machine Studies*, Vol. 7, No. 1, pp. 1-13,
1975.

[5] Mamdani, E.H., "Advances in the
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[6] Mamdani, E.H., "Applications of
fuzzy logic to approximate reasoning using linguistic synthesis," *IEEE
Transactions on Computers*, Vol. 26, No. 12, pp. 1182-1191,
1977.

[7] Schweizer, B. and A. Sklar, "Associative
functions and abstract semi-groups,"* Publ. Math
Debrecen*, 10:69-81, 1963.

[8] Sugeno, M., "Fuzzy measures and fuzzy
integrals: a survey," (M.M. Gupta, G. N. Saridis, and B.R.
Gaines, editors) *Fuzzy Automata and Decision Processes*,
pp. 89-102, North-Holland, NY, 1977.

[9] Sugeno, M.,* Industrial applications
of fuzzy control*, Elsevier Science Pub. Co., 1985.

[10] Yager, R., "On a general class of
fuzzy connectives," *Fuzzy Sets and Systems*,
4:235-242, 1980.

[11] Yager, R. and D. Filev, "Generation
of Fuzzy Rules by Mountain Clustering," *Journal
of Intelligent & Fuzzy Systems*, Vol. 2, No. 3, pp.
209-219, 1994.

[12] Zadeh, L.A., "Fuzzy sets," *Information
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[13] Zadeh, L.A., "Outline of a new approach
to the analysis of complex systems and decision processes," *IEEE
Transactions on Systems, Man, and Cybernetics*, Vol. 3,
No. 1, pp. 28-44, Jan. 1973.

[14] Zadeh, L.A., "The concept of a linguistic
variable and its application to approximate reasoning, Parts 1, 2,
and 3," *Information Sciences*, 1975,
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[15] Zadeh, L.A., "Fuzzy Logic," *Computer*,
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[16] Zadeh, L.A., "Knowledge representation
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