FUZZY LOGIC AND SWARM INTELLIGENCE IN AI
•Download as PPTX, PDF•
1 like•230 views
Presenter: Shivani Soni Course: B-Tech(CSE 7th) Roll no: 13/NR/UT/CS005 Presenting to: Aman Rana The presentation defines fuzzy logic and swarm intelligence. It discusses fuzzy sets and operations on fuzzy sets such as intersection, union, complement, and difference. Swarming behavior is exhibited by animals like termites, birds, and bees to forage, migrate, and defend against predators. Fuzzy logic enhances if-then-else logic with linguistic parameters and fuzzy rules.
Report
Share
Report
Share
Recommended
Fuzzy modelling using sciFLT
Fuzzy logic is a form of many-valued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or high/low. It was introduced by Dr. Lotfi Zadeh and is used in fuzzy decision making systems. SciFLT is a fuzzy logic toolbox for Scilab that allows users to define fuzzy logic systems (FLS) with Mamdani or Sugeno models, including inputs, outputs, membership functions, and rules. It provides tools for visualizing and evaluating FLS.
Fuzzy+logic
This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.
Fuzzy logic &_inference_system
The document discusses fuzzy logic, fuzzy sets, and fuzzy inference systems. It defines fuzzy logic as a form of logic where truth values can be any value between 0 and 1. Fuzzy sets are sets whose elements have degrees of membership between 0 and 1. A fuzzy inference system uses fuzzy set theory and if-then rules to map fuzzy inputs to outputs. The key components of a fuzzy inference system are the fuzzifier, rules base, inference engine, and defuzzifier.
Anfis (1)
This document discusses neural networks and fuzzy logic. It explains that neural networks can learn from data and feedback but are viewed as "black boxes", while fuzzy logic models are easier to comprehend but do not come with a learning algorithm. It then describes how neuro-fuzzy systems combine these two approaches by using neural networks to construct fuzzy rule-based models or fuzzy partitions of the input space. Specifically, it outlines the Adaptive Network-based Fuzzy Inference System (ANFIS) architecture, which is functionally equivalent to fuzzy inference systems and can represent both Sugeno and Tsukamoto fuzzy models using a five-layer feedforward neural network structure.
Fuzzy logic Notes AI CSE 8th Sem
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
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.
Linguistic variable
Neural & Fuzzy Logic On Linguistic Variable.
Modus Ponens,Modus Tollens,Fuzzy Implication Operators
Fuzzy Inference,Fuzzy Proposition,Linguistic variable etc are described here
Chapter 5 - Fuzzy Logic
Fuzzy logic is a flexible machine learning technique that mimics human thought by allowing intermediate values between true and false. It provides a mechanism for interpreting and executing commands based on approximate or uncertain reasoning. Unlike binary logic which can only have true or false values, fuzzy logic uses linguistic variables and degrees of membership to represent concepts that may have a partial truth. Fuzzy systems find applications in automatic control, prediction, diagnosis and user interfaces.
Recommended
Fuzzy modelling using sciFLT
Fuzzy logic is a form of many-valued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or high/low. It was introduced by Dr. Lotfi Zadeh and is used in fuzzy decision making systems. SciFLT is a fuzzy logic toolbox for Scilab that allows users to define fuzzy logic systems (FLS) with Mamdani or Sugeno models, including inputs, outputs, membership functions, and rules. It provides tools for visualizing and evaluating FLS.
Fuzzy+logic
This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.
Fuzzy logic &_inference_system
The document discusses fuzzy logic, fuzzy sets, and fuzzy inference systems. It defines fuzzy logic as a form of logic where truth values can be any value between 0 and 1. Fuzzy sets are sets whose elements have degrees of membership between 0 and 1. A fuzzy inference system uses fuzzy set theory and if-then rules to map fuzzy inputs to outputs. The key components of a fuzzy inference system are the fuzzifier, rules base, inference engine, and defuzzifier.
Anfis (1)
This document discusses neural networks and fuzzy logic. It explains that neural networks can learn from data and feedback but are viewed as "black boxes", while fuzzy logic models are easier to comprehend but do not come with a learning algorithm. It then describes how neuro-fuzzy systems combine these two approaches by using neural networks to construct fuzzy rule-based models or fuzzy partitions of the input space. Specifically, it outlines the Adaptive Network-based Fuzzy Inference System (ANFIS) architecture, which is functionally equivalent to fuzzy inference systems and can represent both Sugeno and Tsukamoto fuzzy models using a five-layer feedforward neural network structure.
Fuzzy logic Notes AI CSE 8th Sem
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
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.
Linguistic variable
Neural & Fuzzy Logic On Linguistic Variable.
Modus Ponens,Modus Tollens,Fuzzy Implication Operators
Fuzzy Inference,Fuzzy Proposition,Linguistic variable etc are described here
Chapter 5 - Fuzzy Logic
Fuzzy logic is a flexible machine learning technique that mimics human thought by allowing intermediate values between true and false. It provides a mechanism for interpreting and executing commands based on approximate or uncertain reasoning. Unlike binary logic which can only have true or false values, fuzzy logic uses linguistic variables and degrees of membership to represent concepts that may have a partial truth. Fuzzy systems find applications in automatic control, prediction, diagnosis and user interfaces.
Fuzzy logic
This document provides an overview of fuzzy logic, including its origins, key concepts, and applications. It discusses how fuzzy logic allows for approximate reasoning and decision making under uncertainty by using linguistic variables and fuzzy set theory. Membership functions are used to characterize fuzzy sets and assign degrees of truth between 0 and 1 rather than binary true/false values. Common fuzzy logic operations like intersection, union, and complement are also covered. Finally, some examples of fuzzy logic control systems are presented, including how they are designed using fuzzy rule bases and inference methods like Mamdani and Sugeno.
Lec 5 uncertainty
Artificial Intelligence lecture notes. AI summarized notes on uncertainty and handling it through fuzzy logic, tipping problem scenarios are seen in it, for reading and may be for self-learning, I think.
Fuzzy logic
This document discusses the application of fuzzy logic to optimal capacitor placement in distribution systems. It begins with definitions of fuzzy logic and fuzzy sets. It then describes the key components of a fuzzy logic system including fuzzification, fuzzy inference rules, and defuzzification. It proposes using power loss reduction index and bus voltage as input variables, and capacitor placement suitability index as the output variable, to determine the optimal locations and sizes of capacitors. The goal is to minimize power losses and maximize annual savings using fuzzy logic techniques.
Fuzzy arithmetic
This document presents information on fuzzy arithmetic and operations. It discusses fuzzy numbers, linguistic variables, and arithmetic operations on fuzzy intervals and fuzzy numbers. Some key points:
- Fuzzy numbers are fuzzy sets with certain properties like being normal, having closed interval alpha-cuts, and bounded support.
- Linguistic variables assign linguistic values like "young" or "old" to numerical variables. They are represented as fuzzy sets.
- Arithmetic operations on fuzzy intervals are defined based on the corresponding operations on their alpha-cuts, which are closed intervals. Properties like commutativity and distributivity are discussed.
- Operations on fuzzy numbers are similarly defined based on the alpha-cuts of the resulting fuzzy
Optimization using soft computing
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.
santosh kumar fuzzy logic presentation
Fuzzy logic is a form of knowledge representation that allows for notions that cannot be defined precisely but depend on context. It uses fuzzy sets where elements have a partial degree of membership between 0 and 1 rather than full membership. Fuzzy logic operators like AND, OR, and NOT are defined for fuzzy sets. Fuzzy logic can be applied to control systems through IF-THEN rules to efficiently and flexibly model imprecise variables like temperature, providing a more resourceful approach than traditional binary logic. Examples of applications include temperature controllers, automotive systems like anti-lock braking, and more.
Fuzzy logic mis
Fuzzy Logic
Where did it begin?
What is Fuzzy Logic?
Fuzzy Logic in Control Systems
Fuzzy Logic in Other Fields
Fuzzy Logic vs. Neural Networks
Fuzzy Logic Benefits
FUZZY LOGIC
This presentation includes what is fuzzy logic, characteristics, membership function with example, fuzzy set theory, De-Morgans Law, Fuzzy logic V/S probability, advantages and disadvantages and application areas of fuzzy logic. This is a presentation is useful for IT students.
Fuzzy Logic ppt
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.
ANFIS
This document provides an overview of adaptive neuro-fuzzy inference systems (ANFIS). It begins by explaining fuzzy sets and fuzzy logic. It then describes the key components of a fuzzy inference system, including fuzzification, a rule base, a database of membership functions, and defuzzification. The document notes that ANFIS integrates neural networks and fuzzy inference by using neural networks to optimize a fuzzy inference system. It outlines the architecture of ANFIS, including its five functional layers that take an input, determine membership functions, apply fuzzy rules, normalize membership functions, and generate an output. Finally, it discusses some benefits of combining fuzzy logic and neural networks.
On fuzzy concepts in engineering ppt. ncce
This document discusses fuzzy concepts and their applications in engineering. It begins with an introduction to fuzzy sets and fuzzy logic as extensions of crisp/classical sets and logic. Examples are provided to illustrate fuzzy membership functions. The key components of a fuzzy logic system are described. An example is given of building a fuzzy controller for room temperature regulation. Various engineering terms that are often used fuzzily, like bandwidth and errors, are listed. Applications of fuzzy logic in various fields like controls, scheduling, and signal analysis are outlined. Three probabilistic divergence measures and their fuzzy analogues are presented. Finally, a model for strategic decision making using these divergence measures is proposed and an illustrative example is provided.
Fuzzy logic ppt
- Fuzzy logic was developed by Lotfi Zadeh to address applications involving subjective or vague data like "attractive person" that cannot be easily analyzed using binary logic. It allows for partial truth values between completely true and completely false.
- Fuzzy logic controllers mimic human decision making and involve fuzzifying inputs, applying fuzzy rules, and defuzzifying outputs. This allows systems to be specified in human terms and automated.
- Fuzzy logic has many applications from industrial process control to consumer products like washing machines and microwaves. It offers an intuitive way to model real-world ambiguities compared to mathematical or logic-based approaches.
Fuzzy Logic Seminar with Implementation
The document provides an overview of fuzzy logic and fuzzy sets. It discusses how fuzzy logic can handle imprecise data unlike classical binary sets. Membership functions assign degrees of membership values between 0 and 1. Fuzzy logic systems use if-then rules and linguistic variables. An example shows how fuzzy logic is used to estimate project risk levels based on funding and staffing levels. Fuzzy logic has been applied in various domains due to its ability to model human reasoning.
Presentation2 Fl
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.
Introduction to fuzzy logic
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
Fuzzy logic
The Fuzzy Logic is discussed with three simple example problems all solved in MATLAB
1. Restaurant Problem
2. Temperature Controller
3. Washing Machine Problem
Fuzzy Sets Introduction With Example
Fuzzy sets allow for gradual membership of elements in a set, rather than binary membership as in classical set theory. Membership is described on a scale of 0 to 1 using a membership function. Fuzzy sets generalize classical sets by treating classical sets as special cases where membership values are restricted to 0 or 1. Fuzzy set theory can model imprecise or uncertain information and is used in domains like bioinformatics. Examples of fuzzy sets include sets like "tall people" where membership in the set is a matter of degree.
Fuzzy logic in approximate Reasoning
The document provides an overview of fuzzy logic and approximate reasoning. It discusses fuzzy sets and membership functions, including different types of membership functions like triangular, trapezoidal, and Gaussian. It also covers fuzzy set operations like union, intersection, and complement. T-norm operators for fuzzy intersection are defined. The document serves as an introduction to key concepts in fuzzy logic.
8709508
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.
Fuzzy logic and fuzzy time series edited
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.
Isla colon 3
Isla Colon es la isla más poblada en el archipiélago de Bocas del Toro, Panamá. Es el punto de embarque para visitar las otras islas y cuenta con el pueblo principal de Bocas del Toro, donde se encuentra el Parque Simón Bolívar. Isla Colon ofrece playas tranquilas para nadar y actividades como ciclismo, deportes acuáticos y natación debido a su clima tropical. Se puede llegar a la isla diariamente desde la ciudad de Panamá.
URSA TECNebook - Il tetto piano caldo con URSA XPS
Manuale tecnico - applicazioni URSA XPS - Isolamento di coperture piane calde - informazioni generali, istruzioni di posa, normative, focus su prodotti, stratigrafie in dwg.
More Related Content
What's hot
Fuzzy logic
This document provides an overview of fuzzy logic, including its origins, key concepts, and applications. It discusses how fuzzy logic allows for approximate reasoning and decision making under uncertainty by using linguistic variables and fuzzy set theory. Membership functions are used to characterize fuzzy sets and assign degrees of truth between 0 and 1 rather than binary true/false values. Common fuzzy logic operations like intersection, union, and complement are also covered. Finally, some examples of fuzzy logic control systems are presented, including how they are designed using fuzzy rule bases and inference methods like Mamdani and Sugeno.
Lec 5 uncertainty
Artificial Intelligence lecture notes. AI summarized notes on uncertainty and handling it through fuzzy logic, tipping problem scenarios are seen in it, for reading and may be for self-learning, I think.
Fuzzy logic
This document discusses the application of fuzzy logic to optimal capacitor placement in distribution systems. It begins with definitions of fuzzy logic and fuzzy sets. It then describes the key components of a fuzzy logic system including fuzzification, fuzzy inference rules, and defuzzification. It proposes using power loss reduction index and bus voltage as input variables, and capacitor placement suitability index as the output variable, to determine the optimal locations and sizes of capacitors. The goal is to minimize power losses and maximize annual savings using fuzzy logic techniques.
Fuzzy arithmetic
This document presents information on fuzzy arithmetic and operations. It discusses fuzzy numbers, linguistic variables, and arithmetic operations on fuzzy intervals and fuzzy numbers. Some key points:
- Fuzzy numbers are fuzzy sets with certain properties like being normal, having closed interval alpha-cuts, and bounded support.
- Linguistic variables assign linguistic values like "young" or "old" to numerical variables. They are represented as fuzzy sets.
- Arithmetic operations on fuzzy intervals are defined based on the corresponding operations on their alpha-cuts, which are closed intervals. Properties like commutativity and distributivity are discussed.
- Operations on fuzzy numbers are similarly defined based on the alpha-cuts of the resulting fuzzy
Optimization using soft computing
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.
santosh kumar fuzzy logic presentation
Fuzzy logic is a form of knowledge representation that allows for notions that cannot be defined precisely but depend on context. It uses fuzzy sets where elements have a partial degree of membership between 0 and 1 rather than full membership. Fuzzy logic operators like AND, OR, and NOT are defined for fuzzy sets. Fuzzy logic can be applied to control systems through IF-THEN rules to efficiently and flexibly model imprecise variables like temperature, providing a more resourceful approach than traditional binary logic. Examples of applications include temperature controllers, automotive systems like anti-lock braking, and more.
Fuzzy logic mis
Fuzzy Logic
Where did it begin?
What is Fuzzy Logic?
Fuzzy Logic in Control Systems
Fuzzy Logic in Other Fields
Fuzzy Logic vs. Neural Networks
Fuzzy Logic Benefits
FUZZY LOGIC
This presentation includes what is fuzzy logic, characteristics, membership function with example, fuzzy set theory, De-Morgans Law, Fuzzy logic V/S probability, advantages and disadvantages and application areas of fuzzy logic. This is a presentation is useful for IT students.
Fuzzy Logic ppt
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.
ANFIS
This document provides an overview of adaptive neuro-fuzzy inference systems (ANFIS). It begins by explaining fuzzy sets and fuzzy logic. It then describes the key components of a fuzzy inference system, including fuzzification, a rule base, a database of membership functions, and defuzzification. The document notes that ANFIS integrates neural networks and fuzzy inference by using neural networks to optimize a fuzzy inference system. It outlines the architecture of ANFIS, including its five functional layers that take an input, determine membership functions, apply fuzzy rules, normalize membership functions, and generate an output. Finally, it discusses some benefits of combining fuzzy logic and neural networks.
On fuzzy concepts in engineering ppt. ncce
This document discusses fuzzy concepts and their applications in engineering. It begins with an introduction to fuzzy sets and fuzzy logic as extensions of crisp/classical sets and logic. Examples are provided to illustrate fuzzy membership functions. The key components of a fuzzy logic system are described. An example is given of building a fuzzy controller for room temperature regulation. Various engineering terms that are often used fuzzily, like bandwidth and errors, are listed. Applications of fuzzy logic in various fields like controls, scheduling, and signal analysis are outlined. Three probabilistic divergence measures and their fuzzy analogues are presented. Finally, a model for strategic decision making using these divergence measures is proposed and an illustrative example is provided.
Fuzzy logic ppt
- Fuzzy logic was developed by Lotfi Zadeh to address applications involving subjective or vague data like "attractive person" that cannot be easily analyzed using binary logic. It allows for partial truth values between completely true and completely false.
- Fuzzy logic controllers mimic human decision making and involve fuzzifying inputs, applying fuzzy rules, and defuzzifying outputs. This allows systems to be specified in human terms and automated.
- Fuzzy logic has many applications from industrial process control to consumer products like washing machines and microwaves. It offers an intuitive way to model real-world ambiguities compared to mathematical or logic-based approaches.
Fuzzy Logic Seminar with Implementation
The document provides an overview of fuzzy logic and fuzzy sets. It discusses how fuzzy logic can handle imprecise data unlike classical binary sets. Membership functions assign degrees of membership values between 0 and 1. Fuzzy logic systems use if-then rules and linguistic variables. An example shows how fuzzy logic is used to estimate project risk levels based on funding and staffing levels. Fuzzy logic has been applied in various domains due to its ability to model human reasoning.
Presentation2 Fl
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.
Introduction to fuzzy logic
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
Fuzzy logic
The Fuzzy Logic is discussed with three simple example problems all solved in MATLAB
1. Restaurant Problem
2. Temperature Controller
3. Washing Machine Problem
Fuzzy Sets Introduction With Example
Fuzzy sets allow for gradual membership of elements in a set, rather than binary membership as in classical set theory. Membership is described on a scale of 0 to 1 using a membership function. Fuzzy sets generalize classical sets by treating classical sets as special cases where membership values are restricted to 0 or 1. Fuzzy set theory can model imprecise or uncertain information and is used in domains like bioinformatics. Examples of fuzzy sets include sets like "tall people" where membership in the set is a matter of degree.
Fuzzy logic in approximate Reasoning
The document provides an overview of fuzzy logic and approximate reasoning. It discusses fuzzy sets and membership functions, including different types of membership functions like triangular, trapezoidal, and Gaussian. It also covers fuzzy set operations like union, intersection, and complement. T-norm operators for fuzzy intersection are defined. The document serves as an introduction to key concepts in fuzzy logic.
8709508
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.
Fuzzy logic and fuzzy time series edited
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.
What's hot (20)
Viewers also liked
Isla colon 3
Isla Colon es la isla más poblada en el archipiélago de Bocas del Toro, Panamá. Es el punto de embarque para visitar las otras islas y cuenta con el pueblo principal de Bocas del Toro, donde se encuentra el Parque Simón Bolívar. Isla Colon ofrece playas tranquilas para nadar y actividades como ciclismo, deportes acuáticos y natación debido a su clima tropical. Se puede llegar a la isla diariamente desde la ciudad de Panamá.
URSA TECNebook - Il tetto piano caldo con URSA XPS
Manuale tecnico - applicazioni URSA XPS - Isolamento di coperture piane calde - informazioni generali, istruzioni di posa, normative, focus su prodotti, stratigrafie in dwg.
cv sharon hall_Dec_16
Sharon Hall has over 20 years of experience in international consultancy on social protection issues. She holds a Masters in Social Work and BSc in Psychology. Her experience includes managing projects in Ukraine, Moldova, Macedonia, Kyrgyzstan, Tajikistan, Kosovo, Palestine, Bosnia and Herzegovina, Montenegro, Serbia, Croatia, Afghanistan, Sierra Leone, Yemen, and Mongolia focused on social assistance, social services, child protection, and developing civil society. She has expertise in policy development, institutional capacity building, and program evaluation.
Estadística básica de la industria turística
Simposio Argentino Español de Turismo 2016
Josep Francesc Valls Giménez. Catedrático URL. Departamento de Marketing en ESADE.
Introduction to Capacitive Sensing Part1
The document introduces capacitive sensing and Cypress' PSoC family. It discusses how capacitive sensing detects conductive objects without physical contact by measuring capacitance. It then describes the PSoC as a programmable system-on-chip that features configurable analog and digital blocks for implementing capacitive sensing applications using its configurable architecture. Finally, it provides an overview of Cypress' capacitive sensing development tools and software.
Qualifying for equipment leasing
Different ways to qualify to finance or lease commercial equipment whether you have good or poor credit
L07 c7 - notes - teacher master - resitance - pp
This document discusses resistance in electricity. It provides background on Georg Ohm and his discovery of Ohm's Law, which relates resistance, voltage, and current. It defines resistance as the heat produced from using voltage to enable current flow. Factors that affect resistance include thickness, length, temperature, and material of the conductor. Resistance increases with thinner/longer conductors and higher temperatures. The document uses an analogy of a water pipe to explain the relationships between voltage, current, and resistance. It concludes by discussing how keeping computers cool is beneficial because heat increases resistance in thin computer circuits.
Media: back to the future
Как изменился потребитель в Казахстане? Что нас ждет в будущем? Медиа-тренды в эпоху потребления.
Equipment Leasing Costs, Payments and Rates (Part 2)
What affects the payments for financing your equipment. Part 2 of 2 covers rates for financing older equipment, leasing equipment with bad credit, and startup financing costs when purchasing business equipment
Ireland mobile consumer survey by Deloitte 2016
Ireland mobile consumer survey - see original post here https://www2.deloitte.com/ie/en/pages/technology-media-and-telecommunications/articles/Mobile-Consumer-Survey-2016.html
Posted for students of my workshops on digital and social communications
Viajes El Corte Inglés
I Simposio Argentino Español de Turismo. Buenos Aires, diciembre de 2016.
Panel: El nuevo canal de intermediación turística (Mayoristas, minoristas, receptivos, DMC, OPC, On Line Travel Agencies), la generación de experiencias turísticas.
Fernando Tomás. Director de Comunicación y Promoción de Viajes El Corte Inglés.
Cuestionario con base a la pelicula de wall e
Este documento presenta un cuestionario sobre la película WALL-E con 30 preguntas. El cuestionario busca reconocer cómo los robots pueden extender las capacidades humanas y clasificar los materiales y su función. Instruye al estudiante a responder las preguntas en Word con ilustraciones y subirlo a un blog educativo anotando los propósitos del cuestionario.
Viewers also liked (14)
URSA TECNebook - Il tetto piano caldo con URSA XPS
URSA TECNebook - Il tetto piano caldo con URSA XPS
Equipment Leasing Costs, Payments and Rates (Part 2)
Equipment Leasing Costs, Payments and Rates (Part 2)
Recently uploaded
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
The Diamonds of 2023-2024 in the IGRA collection
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
DRUGS AND ITS classification slide share
Any substance (other than food) that is used to prevent, diagnose, treat, or relieve symptoms of a
disease or abnormal condition
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS Template 2023-2024 by: Irene S. Rueco
Advanced Java[Extra Concepts, Not Difficult].docx
This is part 2 of my Java Learning Journey. This contains Hashing, ArrayList, LinkedList, Date and Time Classes, Calendar Class and more.
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityAkanksha trivedi rama nursing college kanpur.
Natural birth techniques are various type such as/ water birth , alexender method, hypnosis, bradley method, lamaze method etcExploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar KanvariaHow to Manage Your Lost Opportunities in Odoo 17 CRM
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Community pharmacy- Social and preventive pharmacy UNIT 5
Covered community pharmacy topic of the subject Social and preventive pharmacy for Diploma and Bachelor of pharmacy
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Main Java[All of the Base Concepts}.docx
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Recently uploaded (20)
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
Community pharmacy- Social and preventive pharmacy UNIT 5
Community pharmacy- Social and preventive pharmacy UNIT 5
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
FUZZY LOGIC AND SWARM INTELLIGENCE IN AI
- 1. Presenting by: Shivani Soni Course: B-Tech(CSE 7th) Roll no: 13/NR/UT/CS005 Presenting to: Aman Rana
- 2. Fuzzy Logic Define Fuzzy Logic Why Fuzzy Logic Swarm intellige nce Fuzzy Set Operation What is Swarming Why Do Animal Swarming Can People Swarm
- 3. Artificial Intelligence Non Natural / Simulated Ability to understand think and learn So AI is basically a study of how to make a system which can think ,behave and act exactly or batter than the human being act or react.
- 5. Fuzzy Logic
- 10. The power and flexibility of simple If-Then-Else logic rules is enhanced by adding linguistic parameter. Fuzzy rules are expressed in the form: IF variable IS set THEN action.
- 11. FUZZY SET Let X be a non empty set, A fuzzy set A in X is characterized by its membership function μ A: X -> [0,1], where μ A(x) is t he degree of membership of element x in fuzzy set A for each x ∈ X . A={1.1,2.1,1.0,2.3} A={1,2,3,5}
- 12. OPERATION ON FUZZY SET Intersection of Fuzzy Set The intersection of A and B is defined as (A ∩ B)(x) = min{A(x), B(x)} = A(x)∩B(x), ∀ x ∈ X, as demonstrated in figure
- 13. OPERATION ON FUZZY SET Union of Fuzzy Set The union of A and B is defined as (A ∪ B)(x) = max{A(x), B(x)} = A(x)∪B(x), ∀ x ∈ X, as demonstrated in figure……
- 14. OPERATION ON FUZZY SET Complement of Fuzzy Set The complement of a fuzzy set A is defined as (~ A)(x) = 1 - A(x) as demonstrated in figure……
- 15. OPERATION ON FUZZY SET Difference of Fuzzy Set
- 17. Aggregation of similar animals, generally cruising in the same direction Termites swarm to build colonies Birds swarm to find food Bees swarm to reproduce
- 18. To forage better To migrate As a defense against predators