This document discusses fuzzy logic and fuzzy sets. It introduces fuzzy logic as an extension of classical binary logic that can handle imprecise and vague concepts. Fuzzy sets assign elements a membership value between 0 and 1 rather than crisp inclusion/exclusion. Common fuzzy set operations like union, intersection, complement and containment are defined based on the membership values. Membership functions are used to represent fuzzy sets graphically. Fuzzy logic can model human decision making and common sense in applications where information is uncertain or probabilistic.
This document discusses classical sets and fuzzy sets. It defines classical sets as having distinct elements that are either fully included or excluded from the set. Fuzzy sets allow for gradual membership, with elements having degrees of membership between 0 and 1. Operations like union, intersection, and complement are defined for both classical and fuzzy sets, with fuzzy set operations accounting for degrees of membership. Properties of classical and fuzzy sets and relations are also covered, noting differences like fuzzy sets not following the law of excluded middle.
Fuzzy set theory is an extension of classical set theory that allows for partial membership in a set rather than crisp boundaries. In fuzzy set theory, elements have degrees of membership in a set represented by a membership function between 0 and 1. This allows for modeling of imprecise concepts like "young" where the boundary is ambiguous. Fuzzy set theory is useful for modeling human reasoning and systems that can handle unreliable or incomplete information. Key concepts include fuzzy rules in an if-then format and fuzzy inference using methods like Mamdani inference involving fuzzification, rule evaluation, aggregation, and defuzzification.
This document provides an overview of fuzzy logic and fuzzy set theory. It discusses how fuzzy sets allow for partial membership rather than crisp binary membership in sets. Various membership functions are described, including triangular, trapezoidal, Gaussian, and sigmoidal functions. Properties of fuzzy sets like support, convexity, and symmetry are defined. Finally, fuzzy set operations analogous to classical set operations are mentioned.
The document introduces fuzzy set theory as an extension of classical set theory that allows for elements to have varying degrees of membership rather than binary membership. It discusses key concepts such as fuzzy sets, membership functions, fuzzy logic, fuzzy rules, and fuzzy inference. Fuzzy set theory provides a framework for modeling imprecise and uncertain concepts that are common in human reasoning.
This document provides an overview of fuzzy logic and fuzzy sets. It discusses key concepts such as membership functions, operations on fuzzy sets like complement, intersection and union, properties of fuzzy sets including equality and inclusion, and alpha cuts. The document also introduces fuzzy rules and compares classical rules to fuzzy rules. Finally, it provides examples of applying concepts like support, core and complement to fuzzy sets.
This document defines and explains key concepts in fuzzy set theory, including fuzzy complements, unions, and intersections. It begins with an introduction to fuzzy sets as a generalization of classical sets that allows for gradual membership rather than binary membership. Membership functions assign elements a value between 0 and 1 indicating their degree of belonging to a set. The document then provides definitions and properties of fuzzy complements, unions, intersections, and other related concepts. It concludes with examples of applications of fuzzy set theory such as traffic monitoring systems, appliance controls, and medical diagnosis.
Fuzzy set theory is an extension of classical set theory that allows for partial membership in a set rather than crisp boundaries. In fuzzy set theory, elements have a degree of membership in a set defined by a membership function ranging from 0 to 1 rather than simply belonging or not belonging to a set. Fuzzy sets and logic can model imprecise concepts and are used in applications involving uncertain or ambiguous information like fuzzy controllers.
This document discusses classical sets and fuzzy sets. It defines classical sets as having distinct elements that are either fully included or excluded from the set. Fuzzy sets allow for gradual membership, with elements having degrees of membership between 0 and 1. Operations like union, intersection, and complement are defined for both classical and fuzzy sets, with fuzzy set operations accounting for degrees of membership. Properties of classical and fuzzy sets and relations are also covered, noting differences like fuzzy sets not following the law of excluded middle.
Fuzzy set theory is an extension of classical set theory that allows for partial membership in a set rather than crisp boundaries. In fuzzy set theory, elements have degrees of membership in a set represented by a membership function between 0 and 1. This allows for modeling of imprecise concepts like "young" where the boundary is ambiguous. Fuzzy set theory is useful for modeling human reasoning and systems that can handle unreliable or incomplete information. Key concepts include fuzzy rules in an if-then format and fuzzy inference using methods like Mamdani inference involving fuzzification, rule evaluation, aggregation, and defuzzification.
This document provides an overview of fuzzy logic and fuzzy set theory. It discusses how fuzzy sets allow for partial membership rather than crisp binary membership in sets. Various membership functions are described, including triangular, trapezoidal, Gaussian, and sigmoidal functions. Properties of fuzzy sets like support, convexity, and symmetry are defined. Finally, fuzzy set operations analogous to classical set operations are mentioned.
The document introduces fuzzy set theory as an extension of classical set theory that allows for elements to have varying degrees of membership rather than binary membership. It discusses key concepts such as fuzzy sets, membership functions, fuzzy logic, fuzzy rules, and fuzzy inference. Fuzzy set theory provides a framework for modeling imprecise and uncertain concepts that are common in human reasoning.
This document provides an overview of fuzzy logic and fuzzy sets. It discusses key concepts such as membership functions, operations on fuzzy sets like complement, intersection and union, properties of fuzzy sets including equality and inclusion, and alpha cuts. The document also introduces fuzzy rules and compares classical rules to fuzzy rules. Finally, it provides examples of applying concepts like support, core and complement to fuzzy sets.
This document defines and explains key concepts in fuzzy set theory, including fuzzy complements, unions, and intersections. It begins with an introduction to fuzzy sets as a generalization of classical sets that allows for gradual membership rather than binary membership. Membership functions assign elements a value between 0 and 1 indicating their degree of belonging to a set. The document then provides definitions and properties of fuzzy complements, unions, intersections, and other related concepts. It concludes with examples of applications of fuzzy set theory such as traffic monitoring systems, appliance controls, and medical diagnosis.
Fuzzy set theory is an extension of classical set theory that allows for partial membership in a set rather than crisp boundaries. In fuzzy set theory, elements have a degree of membership in a set defined by a membership function ranging from 0 to 1 rather than simply belonging or not belonging to a set. Fuzzy sets and logic can model imprecise concepts and are used in applications involving uncertain or ambiguous information like fuzzy controllers.
Fuzzy set theory is an extension of classical set theory that allows for partial membership in a set rather than crisp boundaries. In fuzzy set theory, elements have a degree of membership in a set ranging from 0 to 1 rather than simply belonging or not belonging to the set. This allows fuzzy set theory to model imprecise concepts more accurately. Fuzzy sets use membership functions to define the degree of membership for each element. Common membership functions include triangular, trapezoidal, and Gaussian functions. Fuzzy set theory is useful for modeling human reasoning and systems that involve imprecise or uncertain information.
This document discusses fuzzy sets and membership functions. It introduces fuzzy sets as having non-crisp boundaries characterized by membership functions from 0 to 1. Membership functions assign elements a degree of membership based on context. Common membership functions include triangular, trapezoidal, and Gaussian. Fuzzy set operations like union, intersection, and complement can extend classical set operations to accommodate imprecise concepts.
This document provides an overview of fuzzy logic and fuzzy sets. It defines key concepts such as membership functions, operations on fuzzy sets like intersection and union, properties of fuzzy sets including equality and inclusion, and alpha cuts. It also discusses fuzzy rules and how they differ from classical rules by allowing partial truth values. Examples are provided to illustrate fuzzy set concepts and operations. The document is intended as lecture material on fuzzy logic for a course on artificial intelligence and computer science.
This document provides an introduction to fuzzy logic and fuzzy sets. It discusses key concepts such as fuzzy sets having degrees of membership between 0 and 1 rather than binary membership, and fuzzy logic allowing for varying degrees of truth. Examples are given of fuzzy sets representing partially full tumblers and desirable cities to live in. Characteristics of fuzzy sets such as support, crossover points, and logical operations like union and intersection are defined. Applications mentioned include vehicle control systems and appliance control using fuzzy logic to handle imprecise and ambiguous inputs.
Fuzzy logic is a form of logic that accounts for partial truth and degrees of truth. It is based on the concept that the transition between two states is gradual rather than abrupt. Fuzzy logic allows intermediate values between conventional evaluations like true/false, yes/no, high/low. This document discusses classical logic, Boolean logic, fuzzy sets, fuzzy membership functions, fuzzy rules, and fuzzy inference systems. It provides examples of how fuzzy logic can be used to represent imprecise concepts like "around 220V" or "fairly high temperature" through assigning membership values between 0 and 1.
1. The document discusses an emerging approach to computing called soft computing. Soft computing techniques include neural networks, genetic algorithms, machine learning, probabilistic reasoning, and fuzzy logic.
2. Soft computing aims to develop intelligent machines that can solve real-world problems that are difficult to model mathematically. It exploits tolerance for uncertainty and imprecision similar to human decision making.
3. The document then discusses various soft computing techniques in more detail, including neural networks, genetic algorithms, fuzzy logic, and how they differ from traditional hard computing approaches.
The document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It introduces fuzzy concepts in databases, defines a new measure for fuzzy equality, and proposes a fuzzy equi-join based on this measure. It then presents a sort-merge join algorithm called SMFEJ that uses interval ordering to efficiently evaluate the fuzzy equi-join in two phases: sorting relations on join attributes, and joining tuples with overlapping ranges.
Fuzzy logic was initiated in 1965 by Lotfi A. Zadeh as a multivalued logic that allows intermediate values between evaluations like true/false. Fuzzy logic provides a more human-like way of thinking for computer programming. Unlike traditional binary logic, fuzzy systems use degrees of set membership between 0 and 1 rather than crisp 1 or 0 values. Key concepts include fuzzy sets which have membership degrees, and fuzzy operators like complement, union, and intersection that are defined based on membership degrees rather than binary outcomes. Fuzzy logic has been used to control complex systems and for applications like classification.
This document discusses soft computing and fuzzy sets. It begins by defining soft computing as being tolerant of imprecision and focusing on approximation rather than precise outputs. Fuzzy sets are introduced as a tool of soft computing that allow for graded membership in sets rather than binary membership. Key concepts regarding fuzzy sets are explained, including fuzzy logic operations, fuzzy numbers, and fuzzy variables. Linear programming problems are discussed and how they can be modeled as fuzzy linear programming problems to account for imprecision in the coefficients and constraints.
Fuzzy logic provides a means of calculating intermediate values between absolute true and absolute false. It allows partial set membership and handles imprecise data. Fuzzy logic systems use membership functions to determine the degree to which inputs belong to sets and fuzzy inference systems to map inputs to outputs. Fuzzy logic has applications in devices like washing machines and cameras that require handling imprecise variables.
Fuzzy logic allows for intermediate values between absolute true and false. It calculates degrees of membership and handles imprecise conditions like natural language. Areas that use fuzzy logic include appliances, expert systems, and automotive systems like antilock brakes. Fuzzy logic systems involve fuzzifying inputs, applying operators, implicating outputs, aggregating results, and defuzzifying outputs.
This document provides an introduction and overview of CS344: Introduction to Artificial Intelligence course at IIT Bombay. The key points are:
- The course will be taught 3 times a week by Dr. Pushpak Bhattacharyya and TAs. Topics will include search, logic, knowledge representation, neural networks, computer vision, and planning.
- Foundational concepts in AI that will be covered include the Church-Turing hypothesis, Turing machines, the physical symbol system hypothesis, and limits of computability and automation.
- Fuzzy logic will be introduced as a way to model human reasoning with imprecise information using linguistic variables and fuzzy set theory.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It is used to model uncertainty, where membership in a set can range from 0 to 1 rather than being binary. Fuzzy logic allows variables to have a truth value that ranges between 0 and 1. It is used in fuzzy expert systems to represent rules with uncertain or vague linguistic variables.
This document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It begins with an introduction to fuzzy concepts in databases, including representing imprecise data using fuzzy sets and membership functions. It then defines a new measure for fuzzy equality that is used to define a fuzzy equi-join. The document proposes a sort-merge join algorithm that sorts relations based on a partial order of intervals to efficiently evaluate the fuzzy equi-join in two phases: sorting and joining. Experimental results are said to show a significant improvement in efficiency when using this algorithm.
What is Soft Computing ? Difference between Soft Computing and Hard Computing. Classical Sets ,operations on classical sets ,Properties of classical sets
This document provides an overview of basic fuzzy logic concepts including:
- Fuzzy sets allow for partial membership rather than crisp membership as in classical binary logic.
- Membership functions are used to represent fuzzy sets and assign a degree of membership between 0 and 1.
- Common fuzzy logic operations include union, intersection, and complement.
- Fuzzy inference involves using if-then rules to map inputs to outputs based on degrees of membership.
- Applications of fuzzy logic include control systems, decision making, and modeling imprecise concepts.
Dr. Lotfi Ali Asker Zadeh is considered the father of fuzzy logic. In the 1960s and 1970s, he developed the concept of fuzzy sets and fuzzy logic to deal with imprecise data and approximations. Fuzzy logic uses membership values between 0 and 1 rather than binary logic of true and false. It allows partial truth values to model uncertainty. Fuzzy logic has been applied in areas like control systems, decision making, and pattern recognition to handle imprecise concepts.
This document discusses methods for determining membership function values in a fuzzy relational database to optimize image retrieval. It provides an overview of fuzzy relational databases and how they extend conventional databases to allow for imprecise data represented as fuzzy sets. Previous work on a fuzzy database implementation is described that assigns random membership values and adjusts them based on user feedback. Machine learning methods are discussed for automatically improving the membership values through experience. Different methods for constructing membership functions are outlined, including the fuzzy linguistic approach of using natural language terms defined by a user community. The goal is to determine the best approach for setting membership values to satisfy the most users.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It extends conventional binary logic which has only true and false values. Fuzzy logic is used in fuzzy expert systems where rules use linguistic variables and fuzzy membership functions rather than binary logic. A fuzzy expert system fuzzifies inputs, applies inference rules to fuzzy subsets assigned by rules, composes the fuzzy subsets into single fuzzy subsets for outputs, and may defuzzify outputs into crisp values.
Fuzzy logic is a form of multivalued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or 0/1. It provides a mathematical framework for representing uncertainty and imprecision in measurement and human cognition. The document discusses the history of fuzzy logic, key concepts like membership functions and linguistic variables, common fuzzy logic operations, and applications in fields like control systems, home appliances, and cameras. It also notes some drawbacks like difficulty in tuning membership functions and potential confusion with probability theory.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Fuzzy set theory is an extension of classical set theory that allows for partial membership in a set rather than crisp boundaries. In fuzzy set theory, elements have a degree of membership in a set ranging from 0 to 1 rather than simply belonging or not belonging to the set. This allows fuzzy set theory to model imprecise concepts more accurately. Fuzzy sets use membership functions to define the degree of membership for each element. Common membership functions include triangular, trapezoidal, and Gaussian functions. Fuzzy set theory is useful for modeling human reasoning and systems that involve imprecise or uncertain information.
This document discusses fuzzy sets and membership functions. It introduces fuzzy sets as having non-crisp boundaries characterized by membership functions from 0 to 1. Membership functions assign elements a degree of membership based on context. Common membership functions include triangular, trapezoidal, and Gaussian. Fuzzy set operations like union, intersection, and complement can extend classical set operations to accommodate imprecise concepts.
This document provides an overview of fuzzy logic and fuzzy sets. It defines key concepts such as membership functions, operations on fuzzy sets like intersection and union, properties of fuzzy sets including equality and inclusion, and alpha cuts. It also discusses fuzzy rules and how they differ from classical rules by allowing partial truth values. Examples are provided to illustrate fuzzy set concepts and operations. The document is intended as lecture material on fuzzy logic for a course on artificial intelligence and computer science.
This document provides an introduction to fuzzy logic and fuzzy sets. It discusses key concepts such as fuzzy sets having degrees of membership between 0 and 1 rather than binary membership, and fuzzy logic allowing for varying degrees of truth. Examples are given of fuzzy sets representing partially full tumblers and desirable cities to live in. Characteristics of fuzzy sets such as support, crossover points, and logical operations like union and intersection are defined. Applications mentioned include vehicle control systems and appliance control using fuzzy logic to handle imprecise and ambiguous inputs.
Fuzzy logic is a form of logic that accounts for partial truth and degrees of truth. It is based on the concept that the transition between two states is gradual rather than abrupt. Fuzzy logic allows intermediate values between conventional evaluations like true/false, yes/no, high/low. This document discusses classical logic, Boolean logic, fuzzy sets, fuzzy membership functions, fuzzy rules, and fuzzy inference systems. It provides examples of how fuzzy logic can be used to represent imprecise concepts like "around 220V" or "fairly high temperature" through assigning membership values between 0 and 1.
1. The document discusses an emerging approach to computing called soft computing. Soft computing techniques include neural networks, genetic algorithms, machine learning, probabilistic reasoning, and fuzzy logic.
2. Soft computing aims to develop intelligent machines that can solve real-world problems that are difficult to model mathematically. It exploits tolerance for uncertainty and imprecision similar to human decision making.
3. The document then discusses various soft computing techniques in more detail, including neural networks, genetic algorithms, fuzzy logic, and how they differ from traditional hard computing approaches.
The document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It introduces fuzzy concepts in databases, defines a new measure for fuzzy equality, and proposes a fuzzy equi-join based on this measure. It then presents a sort-merge join algorithm called SMFEJ that uses interval ordering to efficiently evaluate the fuzzy equi-join in two phases: sorting relations on join attributes, and joining tuples with overlapping ranges.
Fuzzy logic was initiated in 1965 by Lotfi A. Zadeh as a multivalued logic that allows intermediate values between evaluations like true/false. Fuzzy logic provides a more human-like way of thinking for computer programming. Unlike traditional binary logic, fuzzy systems use degrees of set membership between 0 and 1 rather than crisp 1 or 0 values. Key concepts include fuzzy sets which have membership degrees, and fuzzy operators like complement, union, and intersection that are defined based on membership degrees rather than binary outcomes. Fuzzy logic has been used to control complex systems and for applications like classification.
This document discusses soft computing and fuzzy sets. It begins by defining soft computing as being tolerant of imprecision and focusing on approximation rather than precise outputs. Fuzzy sets are introduced as a tool of soft computing that allow for graded membership in sets rather than binary membership. Key concepts regarding fuzzy sets are explained, including fuzzy logic operations, fuzzy numbers, and fuzzy variables. Linear programming problems are discussed and how they can be modeled as fuzzy linear programming problems to account for imprecision in the coefficients and constraints.
Fuzzy logic provides a means of calculating intermediate values between absolute true and absolute false. It allows partial set membership and handles imprecise data. Fuzzy logic systems use membership functions to determine the degree to which inputs belong to sets and fuzzy inference systems to map inputs to outputs. Fuzzy logic has applications in devices like washing machines and cameras that require handling imprecise variables.
Fuzzy logic allows for intermediate values between absolute true and false. It calculates degrees of membership and handles imprecise conditions like natural language. Areas that use fuzzy logic include appliances, expert systems, and automotive systems like antilock brakes. Fuzzy logic systems involve fuzzifying inputs, applying operators, implicating outputs, aggregating results, and defuzzifying outputs.
This document provides an introduction and overview of CS344: Introduction to Artificial Intelligence course at IIT Bombay. The key points are:
- The course will be taught 3 times a week by Dr. Pushpak Bhattacharyya and TAs. Topics will include search, logic, knowledge representation, neural networks, computer vision, and planning.
- Foundational concepts in AI that will be covered include the Church-Turing hypothesis, Turing machines, the physical symbol system hypothesis, and limits of computability and automation.
- Fuzzy logic will be introduced as a way to model human reasoning with imprecise information using linguistic variables and fuzzy set theory.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It is used to model uncertainty, where membership in a set can range from 0 to 1 rather than being binary. Fuzzy logic allows variables to have a truth value that ranges between 0 and 1. It is used in fuzzy expert systems to represent rules with uncertain or vague linguistic variables.
This document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It begins with an introduction to fuzzy concepts in databases, including representing imprecise data using fuzzy sets and membership functions. It then defines a new measure for fuzzy equality that is used to define a fuzzy equi-join. The document proposes a sort-merge join algorithm that sorts relations based on a partial order of intervals to efficiently evaluate the fuzzy equi-join in two phases: sorting and joining. Experimental results are said to show a significant improvement in efficiency when using this algorithm.
What is Soft Computing ? Difference between Soft Computing and Hard Computing. Classical Sets ,operations on classical sets ,Properties of classical sets
This document provides an overview of basic fuzzy logic concepts including:
- Fuzzy sets allow for partial membership rather than crisp membership as in classical binary logic.
- Membership functions are used to represent fuzzy sets and assign a degree of membership between 0 and 1.
- Common fuzzy logic operations include union, intersection, and complement.
- Fuzzy inference involves using if-then rules to map inputs to outputs based on degrees of membership.
- Applications of fuzzy logic include control systems, decision making, and modeling imprecise concepts.
Dr. Lotfi Ali Asker Zadeh is considered the father of fuzzy logic. In the 1960s and 1970s, he developed the concept of fuzzy sets and fuzzy logic to deal with imprecise data and approximations. Fuzzy logic uses membership values between 0 and 1 rather than binary logic of true and false. It allows partial truth values to model uncertainty. Fuzzy logic has been applied in areas like control systems, decision making, and pattern recognition to handle imprecise concepts.
This document discusses methods for determining membership function values in a fuzzy relational database to optimize image retrieval. It provides an overview of fuzzy relational databases and how they extend conventional databases to allow for imprecise data represented as fuzzy sets. Previous work on a fuzzy database implementation is described that assigns random membership values and adjusts them based on user feedback. Machine learning methods are discussed for automatically improving the membership values through experience. Different methods for constructing membership functions are outlined, including the fuzzy linguistic approach of using natural language terms defined by a user community. The goal is to determine the best approach for setting membership values to satisfy the most users.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It extends conventional binary logic which has only true and false values. Fuzzy logic is used in fuzzy expert systems where rules use linguistic variables and fuzzy membership functions rather than binary logic. A fuzzy expert system fuzzifies inputs, applies inference rules to fuzzy subsets assigned by rules, composes the fuzzy subsets into single fuzzy subsets for outputs, and may defuzzify outputs into crisp values.
Fuzzy logic is a form of multivalued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or 0/1. It provides a mathematical framework for representing uncertainty and imprecision in measurement and human cognition. The document discusses the history of fuzzy logic, key concepts like membership functions and linguistic variables, common fuzzy logic operations, and applications in fields like control systems, home appliances, and cameras. It also notes some drawbacks like difficulty in tuning membership functions and potential confusion with probability theory.
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23 fuzzy lecture ppt basics- new 23.ppt
1. Artificial Intelligence – CS364
Fuzzy Logic
Fuzzy logic
The word "fuzzy" means "vagueness". Fuzziness occurs
when the boundary of a piece of information is not clear-
cut.
• Fuzzy sets have been introduced by Lotfi A. Zadeh
(1965) as an extension of the classical notion of set.
• Classical set theory allows the membership of the
elements in the set in binary terms, a bivalent condition -
an element either belongs or does not belong to the set.
Fuzzy set theory permits the gradual assessment of the
membership of elements in a set, described with the aid of
a membership function valued in the real unit interval [0,
1].
2. Artificial Intelligence – CS364
Fuzzy Logic
Fuzzy set Theory
• Example: Words like young, tall, good, or high are fuzzy.
− There is no single quantitative value which defines the
term young. −
• For some people, age 25 is young, and for others, age 35
is young. − The concept young has no clean boundary. −
Age 1 is definitely young and age 100 is definitely not
young; − Age 35 has some possibility of being young and
usually depends on the context in which it is being
considered
12/28/2023
3. Artificial Intelligence – CS364
Fuzzy Logic
In real world, there exists much fuzzy knowledge;
Knowledge that is vague, imprecise, uncertain,
ambiguous, inexact, or probabilistic in nature.
Human thinking and reasoning frequently involve
fuzzy information, originating from inherently
inexact human concepts. Humans, can give
satisfactory answers, which are probably true.
However, our systems are unable to answer many
questions. The reason is, most systems are
designed based upon classical set theory and two-
valued logic which is unable to cope with
unreliable and incomplete information and give
expert opinions.
4. Artificial Intelligence – CS364
Fuzzy Logic
Fuzzy logic
Fuzzy logic is determined as a set of mathematical principles for knowledge
representation based on degree of member ship functions rather than on
crisp membership function of classical binary logic
Fuzzy logic reflects how people think . It attempts to model our sense of
words ,our decision making and our common sense.
Unlike two valued Boolean logic, fuzzy logic is multi-valued . It deals with
degree of membership and degrees of truth.
5. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Contents
• Fuzzy set-A fuzzy set is simply defined as a set with fuzzy
boundaries.
• a membership function for a fuzzy set A on the universe
of discourse X is defined as µA:X → [0,1], where each
element of X is mapped to a value between 0 and 1. This
value, called membership value or degree of membership,
quantifies the grade of membership of the element in X to
the fuzzy set A.
• Membership functions allow us to graphically represent a
fuzzy set. The x axis represents the universe of discourse,
whereas the y axis represents the degrees of membership in
the [0,1] interval.
6. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Characteristics of Fuzzy Sets
• The classical set theory developed in the late 19th century
by Georg Cantor describes how crisp sets can interact.
These interactions are called operations.
• Also fuzzy sets have well defined properties.
• These properties and operations are the basis on which the
fuzzy sets are used to deal with uncertainty on the one
hand and to represent knowledge on the other.
7. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Note: Membership Functions
• For the sake of convenience, usually a fuzzy set is denoted
as:
A = A(xi)/xi + …………. + A(xn)/xn
where A(xi)/xi (a singleton) is a pair “grade of
membership” element, that belongs to a finite universe of
discourse:
A = {x1, x2, .., xn}
8. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Example to represent fuzzy set
(i) ‘Definitely tall’ may be represented as ‘tallness having value 1’
(ii) ‘Not at all tall’ may be represented as ‘Tallness having value 0’
(iii) ‘A little bit tall’ may be represented as ‘tallness having value say .2’.
(iv) ‘Slightly tall’ may be represented as ‘tallness having value say .4’.
(v) ‘Reasonably tall’ may be represented as ‘tallness having value say .7’.
and so on.
Similarly, the values of other concepts or, rather, other linguistic variables like sweet,
good, beautiful, etc. may be considered in terms of real numbers between 0 and 1.
Coming back to the imprecise concept of tall, let us think of five male persons of an
organisation, viz., Mohan, Sohan, John, Abdul, Abrahm, with heights 5' 2”, 6' 4”, 5' 9”,
4' 8”, 5' 6” respectively.
Then had we talked only of crisp set of tall persons, we would have denoted the Set of
tall persons in the organisation = {Sohan}
But, a fuzzy set, representing tall persons, include all the persons alongwith respective
degrees of tallness. Thus, in terms of fuzzy sets, we write:
Tall = {Mohan/.2; Sohan/1; John/.7; Abdul/0; Abrahm/.4}.
The values .2, 1, .7, 0, .4 are called membership values or degrees: Note:
9. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Relation
Relation on fuzzy sets refers to a way of associating elements
from one fuzzy set with elements from another fuzzy set. This
concept extends the idea of relations from classical set theory to
accommodate the notion of degrees of membership in fuzzy
sets.
A fuzzy relation R between two fuzzy sets A and B is defined
by a set of ordered pairs (x,y,μR(x,y)), where x is an element
from A, y is an element from B, and μR(x,y) is a membership
function indicating the degree to which x is related to y in the
fuzzy relation R.
In other words, the membership function μR(x,y) assigns a
degree of membership to the pair (x,y) in the fuzzy relation R.
This degree of membership can be interpreted as the strength or
degree of the relationship between x and y.
11. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Operations of Fuzzy Sets
Intersection Union
Complement
Not A
A
Containment
A
A
B
B
A B
A
A B
13. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Operations :-
Fuzzy relations, like crisp relations, can undergo various operations that
involve combining, modifying, or analyzing these relations. Here are
some common operations on fuzzy relations:
Max-Min Composition:
Union
Intersection
Compliment
Containment
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Fuzzy Logic
12/28/2023
Complement
• Crisp Sets: Who does not belong to the set?
• Fuzzy Sets: How much do elements not belong to the set?
• The complement of a set is an opposite of this set. For
example, if we have the set of tall men, its complement is
the set of NOT tall men. When we remove the tall men set
from the universe of discourse, we obtain the complement.
• If A is the fuzzy set, its complement A can be found as
follows:
A(x) = 1 A(x)
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Fuzzy Logic
12/28/2023
Containment
• Crisp Sets: Which sets belong to which other sets?
• Fuzzy Sets: Which sets belong to other sets?
• Similar to a Chinese box, a set can contain other sets. The
smaller set is called the subset. For example, the set of tall
men contains all tall men; very tall men is a subset of tall
men. However, the tall men set is just a subset of the set of
men. In crisp sets, all elements of a subset entirely belong
to a larger set. In fuzzy sets, however, each element can
belong less to the subset than to the larger set. Elements of
the fuzzy subset have smaller memberships in it than in the
larger set.
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Fuzzy Logic
12/28/2023
Intersection
• Crisp Sets: Which element belongs to both sets?
• Fuzzy Sets: How much of the element is in both sets?
• In classical set theory, an intersection between two sets contains the
elements shared by these sets. For example, the intersection of the set
of tall men and the set of fat men is the area where these sets overlap.
In fuzzy sets, an element may partly belong to both sets with different
memberships.
• A fuzzy intersection is the lower membership in both sets of each
element. The fuzzy intersection of two fuzzy sets A and B on universe
of discourse X:
AB(x) = min [A(x), B(x)] = A(x) B(x),
where xX
18. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Union
• Crisp Sets: Which element belongs to either set?
• Fuzzy Sets: How much of the element is in either set?
• The union of two crisp sets consists of every element that falls into
either set. For example, the union of tall men and fat men contains all
men who are tall OR fat.
• In fuzzy sets, the union is the reverse of the intersection. That is, the
union is the largest membership value of the element in either set.
The fuzzy operation for forming the union of two fuzzy sets A and B
on universe X can be given as:
AB(x) = max [A(x), B(x)] = A(x) B(x),
where xX
20. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Operations of Fuzzy Sets
Complement
0
x
1
(x)
0
x
1
Containment
0
x
1
0
x
1
A B
Not A
A
Intersection
0
x
1
0
x
A B
Union
0
1
A B
A B
0
x
1
0
x
1
B
A
B
A
(x)
(x) (x)
24. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Properties of Fuzzy Sets
• Equality of two fuzzy sets
• Inclusion of one set into another fuzzy set
• Cardinality of a fuzzy set
• An empty fuzzy set
• -cuts (alpha-cuts)
25. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Equality
• Fuzzy set A is considered equal to a fuzzy set B, IF AND
ONLY IF (iff):
A(x) = B(x), xX
A = 0.3/1 + 0.5/2 + 1/3
B = 0.3/1 + 0.5/2 + 1/3
therefore A = B
26. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Inclusion
• Inclusion of one fuzzy set into another fuzzy set. Fuzzy set
A X is included in (is a subset of) another fuzzy set, B
X:
A(x) B(x), xX
Consider X = {1, 2, 3} and sets A and B
A = 0.3/1 + 0.5/2 + 1/3;
B = 0.5/1 + 0.55/2 + 1/3
then A is a subset of B, or A B
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Fuzzy Logic
12/28/2023
Cardinality
• Cardinality of a non-fuzzy set, Z, is the number of elements in Z. BUT
the cardinality of a fuzzy set A, the so-called SIGMA COUNT, is
expressed as a SUM of the values of the membership function of A,
A(x):
cardA = A(x1) + A(x2) + … A(xn) = ΣA(xi), for i=1..n
Consider X = {1, 2, 3} and sets A and B
A = 0.3/1 + 0.5/2 + 1/3;
B = 0.5/1 + 0.55/2 + 1/3
cardA = 1.8
cardB = 2.05
28. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Empty Fuzzy Set
• A fuzzy set A is empty, IF AND ONLY IF:
A(x) = 0, xX
Consider X = {1, 2, 3} and set A
A = 0/1 + 0/2 + 0/3
then A is empty
29. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Alpha-cut
• An -cut or -level set of a fuzzy set A X is an ORDINARY SET
A X, such that:
A={A(x), xX}.
Consider X = {1, 2, 3} and set A
A = 0.3/1 + 0.5/2 + 1/3
then A0.5 = {2, 3},
A0.1 = {1, 2, 3},
A1 = {3}
30. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Set Normality
• A fuzzy subset of X is called normal if there exists at least one
element xX such that A(x) = 1.
• A fuzzy subset that is not normal is called subnormal.
• All crisp subsets except for the null set are normal. In fuzzy set theory,
the concept of nullness essentially generalises to subnormality.
• The height of a fuzzy subset A is the large membership grade of an
element in A
height(A) = maxx(A(x))
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Fuzzy Logic
12/28/2023
Fuzzy Sets Core and Support
• Assume A is a fuzzy subset of X:
• the support of A is the crisp subset of X consisting of all
elements with membership grade:
supp(A) = {x A(x) 0 and xX}
• the core of A is the crisp subset of X consisting of all
elements with membership grade:
core(A) = {x A(x) = 1 and xX}
32. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Set Math Operations
• aA = {aA(x), xX}
Let a =0.5, and
A = {0.5/a, 0.3/b, 0.2/c, 1/d}
then
Aa = {0.25/a, 0.15/b, 0.1/c, 0.5/d}
• Aa = {A(x)a, xX}
Let a =2, and
A = {0.5/a, 0.3/b, 0.2/c, 1/d}
then
Aa = {0.25/a, 0.09/b, 0.04/c, 1/d}
• …
33. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Sets Examples
• Consider two fuzzy subsets of the set X,
X = {a, b, c, d, e }
referred to as A and B
A = {1/a, 0.3/b, 0.2/c 0.8/d, 0/e}
and
B = {0.6/a, 0.9/b, 0.1/c, 0.3/d, 0.2/e}
37. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Rules
• In 1973, Lotfi Zadeh published his second most influential paper. This
paper outlined a new approach to analysis of complex systems, in
which Zadeh suggested capturing human knowledge in fuzzy rules.
• A fuzzy rule can be defined as a conditional statement in the form:
IF x is A
THEN y is B
• where x and y are linguistic variables; and A and B are linguistic values
determined by fuzzy sets on the universe of discourses X and Y,
respectively.
38. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Classical Vs Fuzzy Rules
• A classical IF-THEN rule uses binary logic, for example,
Rule: 1 Rule: 2
IF speed is > 100 IF speed is < 40
THEN stopping_distance is long THEN stopping_distance is short
• The variable speed can have any numerical value between 0 and 220
km/h, but the linguistic variable stopping_distance can take either
value long or short. In other words, classical rules are expressed in the
black-and-white language of Boolean logic.
39. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Classical Vs Fuzzy Rules
• We can also represent the stopping distance rules in a fuzzy form:
Rule: 1 Rule: 2
IF speed is fast IF speed is slow
THEN stopping_distance is long THEN stopping_distance is short
• In fuzzy rules, the linguistic variable speed also has the range (the
universe of discourse) between 0 and 220 km/h, but this range includes
fuzzy sets, such as slow, medium and fast. The universe of discourse
of the linguistic variable stopping_distance can be between 0 and 300
m and may include such fuzzy sets as short, medium and long.
40. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Classical Vs Fuzzy Rules
• Fuzzy rules relate fuzzy sets.
• In a fuzzy system, all rules fire to some extent, or in other
words they fire partially. If the antecedent is true to some
degree of membership, then the consequent is also true to
that same degree.
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Fuzzy Logic
12/28/2023
DEFINITIONS
A. Definitions
1. Sets
a. Classical sets – either an element belongs to the set or it
does not. For example, for the set of integers, either an
integer is even or it is not (it is odd). However, either you
are in the USA or you are not. What about flying into
USA, what happens as you are crossing? Another example
is for black and white photographs, one cannot say either a
pixel is white or it is black. However, when you digitize a
b/w figure, you turn all the b/w and gray scales into 256
discrete tones.
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Fuzzy Logic
12/28/2023
Classical sets
Classical sets are also called crisp (sets).
Lists: A = {apples, oranges, cherries, mangoes}
A = {a1,a2,a3 }
A = {2, 4, 6, 8, …}
Formulas: A = {x | x is an even natural number}
A = {x | x = 2n, n is a natural number}
Membership or characteristic function
A
x
A
x
x
A if
0
if
1
)
(
43. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Definitions – fuzzy sets
b. Fuzzy sets – admits gradation such as all tones between
black and white. A fuzzy set has a graphical description
that expresses how the transition from one to another takes
place. This graphical description is called a membership
function.
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Fuzzy Logic
12/28/2023
Firing Fuzzy Rules
• A fuzzy rule can have multiple antecedents, for example:
IF project_duration is long
AND project_staffing is large
AND project_funding is inadequate
THEN risk is high
IF service is excellent
OR food is delicious
THEN tip is generous
• The consequent of a fuzzy rule can also include multiple parts, for
instance:
IF temperature is hot
THEN hot_water is reduced;
cold_water is increased
48. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Sets Example
• Air-conditioning involves the delivery of air which can be warmed or
cooled and have its humidity raised or lowered.
• An air-conditioner is an apparatus for controlling, especially lowering,
the temperature and humidity of an enclosed space. An air-conditioner
typically has a fan which blows/cools/circulates fresh air and has
cooler and the cooler is under thermostatic control. Generally, the
amount of air being compressed is proportional to the ambient
temperature.
• Consider Johnny’s air-conditioner which has five control switches:
COLD, COOL, PLEASANT, WARM and HOT. The corresponding
speeds of the motor controlling the fan on the air-conditioner has the
graduations: MINIMAL, SLOW, MEDIUM, FAST and BLAST.
49. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Sets Example
• The rules governing the air-conditioner are as follows:
RULE 1:
IF TEMP is COLD THEN SPEED is MINIMAL
RULE 2:
IF TEMP is COOL THEN SPEED is SLOW
RULE 3:
IF TEMP is PLEASANT THEN SPEED is MEDIUM
RULE 4:
IF TEMP is WARM THEN SPEED is FAST
RULE 5:
IF TEMP is HOT THEN SPEED is BLAST
50. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Sets Example
The temperature graduations are
related to Johnny’s perception of
ambient temperatures.
where:
Y : temp value belongs to the set
(0<A(x)<1)
Y* : temp value is the ideal member to
the set (A(x)=1)
N : temp value is not a member of the
set (A(x)=0)
Temp
(0C).
COLD COOL PLEASANT WARM HOT
0 Y* N N N N
5 Y Y N N N
10 Y Y N N N
12.5 N Y* N N N
15 N Y N N N
17.5 N N Y* N N
20 N N N Y N
22.5 N N N Y* N
25 N N N Y N
27.5 N N N N Y
30 N N N N Y*
51. Artificial Intelligence – CS364
Fuzzy Logic
12/28/2023
Fuzzy Sets Example
Johnny’s perception of the speed of the
motor is as follows:
where:
Y : temp value belongs to the set
(0<A(x)<1)
Y* : temp value is the ideal member to
the set (A(x)=1)
N : temp value is not a member of the
set (A(x)=0)
Rev/sec
(RPM)
MINIMAL SLOW MEDIUM FAST BLAST
0 Y* N N N N
10 Y N N N N
20 Y Y N N N
30 N Y* N N N
40 N Y N N N
50 N N Y* N N
60 N N N Y N
70 N N N Y* N
80 N N N Y Y
90 N N N N Y
100 N N N N Y*
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Fuzzy Logic
12/28/2023
Exercises
For
A = {0.2/a, 0.4/b, 1/c, 0.8/d, 0/e}
B = {0/a, 0.9/b, 0.3/c, 0.2/d, 0.1/e}
Draw the Fuzzy Graph of A and B
Then, calculate the following:
- Support, Core, Cardinality, and Complement for A and B
independently
- Union and Intersection of A and B
- the new set C, if C = A2
- the new set D, if D = 0.5 B
- the new set E, for an alpha cut at A0.5