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Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. Just as in fuzzy set theory the set membership values can range (inclusively) between 0 and 1, in fuzzy logic the degree of truth of a statement can range between 0 and 1 and is not constrained to the two truth values {true, false} as in classic predicate logic. And when linguistic variables are used, these degrees may be managed by specific functions, as discussed below.

The term "fuzzy logic" emerged in the development of the theory of fuzzy sets by Lotfi Zadeh. The first paper on Fuzzy Logic (defined as a multivalued logic based upon set theory) was published by R.H. Wilkinson in 1963 following his first proposals in his 1961 Electrical Engineering master thesis. He was the first one to redefine the earlier multivalued logics in terms of set theory. The main purpose of his paper was to show how any mathematical function could be simulated using hardwired analog electronic circuits based upon what he called analog logic. He did this by first creating various linear voltage ramps which were then selected in a "logic block" using diodes and resistor circuits which implemented the maximum and minimum Fuzzy Logic rules of the INCLUSIVE OR and the AND operations respectively. He called his logic analog logic.

In 1965 Lotfi Zadeh (then an Electrical Engineering systems professor) axiomatized the logic of Wilkinson without the electrical circuits and without giving Wilkinson any credit. Fuzzy Logic has been applied to diverse fields from control theory to artificial intelligence, yet still remains controversial among most statisticians who prefer probabilistic logic and some control engineers who prefer traditional two valued logic.

A probabilistic setting would first define a scalar variable for the fullness of the glass, and second, conditional distributions describing the probability that someone would call the glass full given a specific fullness level. This model, however, has no sense without accepting occurrence of some event, e.g. that after a few minutes, the glass will be half empty. Note that the conditioning can be achieved by having a specific observer that randomly selects the label for the glass, a distribution over deterministic observers, or both. Consequently, probability has nothing in common with fuzziness, these are simply different concepts which superficially seem similar because of using the same interval of real numbers [0, 1].

In this image, the meaning of the expressions cold, warm, and hot is represented by functions mapping a temperature scale. A point on that scale has three "truth values" — one for each of the three functions. The vertical line in the image represents a particular temperature that the three arrows (truth values) gauge. Since the red arrow points to zero, this temperature may be interpreted as "not hot". The orange arrow (pointing at 0.2) may describe it as "slightly warm" and the blue arrow (pointing at 0.8) "fairly cold".

A linguistic variable such as age may have a value such as young or its antonym old. However, the great utility of linguistic variables is that they can be modified via linguistic hedges applied to primary terms. The linguistic hedges can be associated with certain functions. For example, L. A. Zadeh proposed to take the square of the membership function. This model, however, does not work properly. For more details, see the references.

Rules are usually expressed in the form:

IF variable IS property THEN action

For example, an extremely simple temperature regulator that uses a fan might look like this:

IF temperature IS very cold THEN stop fan

IF temperature IS cold THEN turn down fan

IF temperature IS normal THEN maintain level

IF temperature IS hot THEN speed up fan

Notice there is no "ELSE". All of the rules are evaluated, because the temperature might be "cold" and "normal" at the same time to different degrees.

The AND, OR, and NOT operators of boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement; when they are defined this way, they are called the Zadeh operators, because they were first defined as such in Zadeh's original papers. So for the fuzzy variables x and y:

NOT x = (1 - truth(x))

x AND y = minimum(truth(x), truth(y))

x OR y = maximum(truth(x), truth(y))

There are also other operators, more linguistic in nature, called hedges that can be applied. These are generally adverbs such as "very", or "somewhat", which modify the meaning of a set using a mathematical formula.

In application, the programming language Prolog is well geared to implementing fuzzy logic with its facilities to set up a database of "rules" which are queried to deduct logic. This sort of programming is known as logic programming.

Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational database, FRDB, appeared in Maria Zemankova's dissertation. After, some other models arose like the Buckles-Petry model, the Prade-Testemale Model, the Umano-Fukami model or the GEFRED model by J.M. Medina, M.A. Vila et al. In the context of fuzzy databases, some fuzzy querying languages have been defined, highlighting the SQLf by P. Bosc et al. and the FSQL by J. Galindo et al. These languages define some structures in order to include fuzzy aspects in the SQL statements, like fuzzy conditions, fuzzy comparators, fuzzy constants, fuzzy constraints, fuzzy thresholds, linguistic labels and so on.

- If a male is 1.8 meters, consider him as tall:

IF male IS true AND height >= 1.8 THEN is_tall IS true; is_short IS false

- The fuzzy rules do not make sharp distinction between tall and short, that is not so realistic:

IF height <= medium male THEN is_short IS agree somewhat

IF height >= medium male THEN is_tall IS agree somewhat

In the fuzzy case, there are no such heights like 1.83 meters, but there are fuzzy values, like the following assignments:

dwarf male = [0, 1.3] m

short male = [1.3, 1.5] m

medium male = [1.5, 1.8] m

tall male = [1.8, 2.0] m

giant male > 2.0 m

For the consequent, there are also not only two values, but five, say:

agree not = 0

agree little = 1

agree somewhat = 2

agree a lot = 3

agree fully = 4

In the binary, or "crisp", case, a person of 1.79 meters of height is considered medium. If another person is 1.8 meters or 2.25 meters, these persons are considered tall.

The crisp example differs deliberately from the fuzzy one. We did not put in the antecedent

IF male >= agree somewhat AND ...

as gender is often considered as a binary information. So, it is not so complex as being tall.

The most important propositional fuzzy logics are:

- Monoidal t-norm-based propositional fuzzy logic MTL is an axiomatization of logic where conjunction is defined by a left continuous t-norm, and implication is defined as the residuum of the t-norm. Its models correspond to MTL-algebras that are prelinear commutative bounded integral residuated lattices.
- Basic propositional fuzzy logic BL is an extension of MTL logic where conjunction is defined by a continuous t-norm, and implication is also defined as the residuum of the t-norm. Its models correspond to BL-algebras.
- Łukasiewicz fuzzy logic is an extension of basic fuzzy logic BL where standard conjunction is the Łukasiewicz t-norm. It has the axioms of basic fuzzy logic plus an axiom of double negation, and its models correspond to MV-algebras.
- Gödel fuzzy logic is a special case of basic fuzzy logic where conjunction is Gödel t-norm. It has the axioms of basic logic plus an axiom of idempotence of conjunction, and its models are called G-algebras.
- Product fuzzy logic is a special case of basic fuzzy logic where conjunction is product t-norm. It has the axioms of basic logic plus another axiom, and its models are called product algebras.
- Fuzzy logic with evaluated syntax (sometimes also called Pavelka's logic), denoted by EVŁ, is a further generalization of mathematical fuzzy logic. While the above kinds of fuzzy logic have traditional syntax and many-valued semantics, in EVŁ is evaluated also syntax. This means that each formula has an evaluation. Axiomatization of EVŁ stems from Łukasziewicz fuzzy logic. A generalization of classical Godel completeness theorem is provable in EVŁ.

Theorem. Any axiomatizable fuzzy theory is recursively enumerable. In particular, the fuzzy set of logically true formulas is recursively enumerable in spite of the fact that the crisp set of valid formulas is not recursively enumerable, in general. Moreover, any axiomatizable and complete theory is decidable.

It is an open question to give supports for a Church thesis for fuzzy logic claiming that the proposed notion of recursive enumerability for fuzzy subsets is the adequate one. To this aim, further investigations on the notions of fuzzy grammar and fuzzy Turing machine should be necessary (see for example Wiedermann's paper). Another open question is to start from this notion to find an extension of Gödel’s theorems to fuzzy logic.

- Automobile and other vehicle subsystems, such as automatic transmissions, ABS and cruise control (e.g. Tokyo monorail)
- Air conditioners
- The Massive engine used in the Lord of the Rings films, which helped huge scale armies create random, yet orderly movements
- Cameras
- Digital image processing, such as edge detection
- Rice cookers
- Dishwashers
- Elevators
- Washing machines and other home appliances
- Video game artificial intelligence
- Language filters on message boards and chat rooms for filtering out offensive text
- Pattern recognition in Remote Sensing
- Fuzzy logic has also been incorporated into some microcontrollers and microprocessors, for instance, the Freescale 68HC12.

- Fuzzy logic is not any less precise than any other form of logic: it is an organized and mathematical method of handling inherently imprecise concepts. The concept of "coldness" cannot be expressed in an equation, because although temperature is a quantity, "coldness" is not. However, people have an idea of what "cold" is, and agree that there is no sharp cutoff between "cold" and "not cold", where something is "cold" at N degrees but "not cold" at N+1 degrees — a concept classical logic cannot easily handle due to the principle of bivalence. The result has no set answer so it is believed to be a 'fuzzy' answer. Fuzzy logic simply provides a mathematical model of the vagueness which is manifested in the above example.Fuzzy logic is a new way of expressing probability.

- Fuzzy logic and probability are different ways of expressing uncertainty. While both fuzzy logic and probability theory can be used to represent subjective belief, fuzzy set theory uses the concept of fuzzy set membership (i.e. how much a variable is in a set), probability theory uses the concept of subjective probability (i.e. how probable do I think that a variable is in a set). While this distinction is mostly philosophical, the fuzzy-logic-derived possibility measure is inherently different from the probability measure, hence they are not directly equivalent. However, many statisticians are persuaded by the work of Bruno de Finetti that only one kind of mathematical uncertainty is needed and thus fuzzy logic is unnecessary. On the other hand, Bart Kosko argues that probability is a subtheory of fuzzy logic, as probability only handles one kind of uncertainty. He also claims to have proven a derivation of Bayes' theorem from the concept of fuzzy subsethood. Lotfi Zadeh argues that fuzzy logic is different in character from probability, and is not a replacement for it. He fuzzified probability to fuzzy probability and also generalized it to what is called possibility theory. Other approaches to uncertainty include Dempster-Shafer theory and rough sets.

- Note, however, that fuzzy logic is not controversial to probability but rather complementary (cf. )Fuzzy logic will be difficult to scale to larger problems.

- This criticism is mainly because there exist problems with conditional possibility, the fuzzy set theory equivalent of conditional probability (see Halpern (2003), Section 3.8). This makes it difficult to perform inference. However there have not been many studies comparing fuzzy-based systems with probabilistic ones.

- Artificial intelligence
- Artificial neural network
- Neuro-fuzzy
- Biologically-inspired computing
- Combs method
- Concept mining
- Contextualism
- Control system
- Defuzzification
- Dynamic logic
- Expert system
- False dilemma
- Fuzzy subalgebra
- Fuzzy associative matrix
- FuzzyCLIPS expert system
- Fuzzy concept
- Fuzzy Control Language
- Fuzzy control system
- Fuzzy electronics
- Fuzzy set
- Machine learning
- Multi-valued logic
- Paradox of the heap
- Perspectivism
- Pattern recognition
- Rough set

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- Formal fuzzy logic - article at Citizendium
- Fuzzy Logic - article at Scholarpedia
- Modeling With Words - article at Scholarpedia
- Fuzzy logic - article at Stanford Encyclopedia of Philosophy
- Putting Fuzzy Logic To Work PC AI Mar/Apr, 2002 An Introduction to Fuzzy Rules
- Fuzzy Math - Beginner level introduction to Fuzzy Logic.
- Fuzzy Logic and the Internet of Things: I-o-T

Links pages

- Web page about FSQL: References and links about FSQL

Software & tools

- pyFuzzyLib: Open Source Library to write software with fuzzy logic (Python)
- RockOn Fuzzy: Open Source Fuzzy Control And Simulation Tool (Java)
- Free Educational Software and Application Notes
- InrecoLAN FuzzyMath, Fuzzy logic add-in for OpenOffice.org Calc
- JFuzzyLogic: Open Source Fuzzy Logic Package + FCL (sourceforge, java)
- Open fuzzy logic based inference engine and data mining web service based on Metarule
- Open Source Software "mbFuzzIT" (Java)

Tutorials

- Fuzzy Logic Tutorial
- Another Fuzzy Logic Tutorial with MATLAB/Simulink Tutorial
- Fuzzy logic in your game - tutorial aimed towards game programming.
- Simple test to check how well you understand it

Applications

- Research article that describes how industrial foresight could be integrated into capital budgeting with intelligent agents and Fuzzy Llogic
- A doctoral dissertation describing how Fuzzy Logic can be applied in profitability analysis of very large industrial investments

Research Centres

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Last updated on Monday September 29, 2008 at 15:15:17 PDT (GMT -0700)

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This article is licensed under the GNU Free Documentation License.

Last updated on Monday September 29, 2008 at 15:15:17 PDT (GMT -0700)

View this article at Wikipedia.org - Edit this article at Wikipedia.org - Donate to the Wikimedia Foundation

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