Non-classical logic | Fuzzy logic
A fuzzy concept is a kind of concept of which the boundaries of application can vary considerably according to context or conditions, instead of being fixed once and for all. This means the concept is vague in some way, lacking a fixed, precise meaning, without however being unclear or meaningless altogether. It has a definite meaning, which can be made more precise only through further elaboration and specification - including a closer definition of the context in which the concept is used. The study of the characteristics of fuzzy concepts and fuzzy language is called fuzzy semantics. The inverse of a "fuzzy concept" is a "crisp concept" (i.e. a precise concept). A fuzzy concept is understood by scientists as a concept which is "to an extent applicable" in a situation. That means the concept has gradations of significance or unsharp (variable) boundaries of application. A fuzzy statement is a statement which is true "to some extent", and that extent can often be represented by a scaled value. The term is also used these days in a more general, popular sense – in contrast to its technical meaning – to refer to a concept which is "rather vague" for any kind of reason. In the past, the very idea of reasoning with fuzzy concepts faced considerable resistance from academic elites. They did not want to endorse the use of imprecise concepts in research or argumentation. Yet although people might not be aware of it, the use of fuzzy concepts has risen gigantically in all walks of life from the 1970s onward. That is mainly due to advances in electronic engineering, fuzzy mathematics and digital computer programming. The new technology allows very complex inferences about "variations on a theme" to be anticipated and fixed in a program. New neuro-fuzzy computational methods make it possible to identify, measure and respond to fine gradations of significance with great precision. It means that practically useful concepts can be coded and applied to all kinds of tasks, even if ordinarily these concepts are never precisely defined. Nowadays engineers, statisticians and programmers often represent fuzzy concepts mathematically, using fuzzy logic, fuzzy values, fuzzy variables and fuzzy sets. (Wikipedia).
What Is Fuzzy Logic? | Fuzzy Logic, Part 1
This video introduces fuzzy logic and explains how you can use it to design a fuzzy inference system (FIS), which is a powerful way to use human experience to design complex systems. Designing a FIS does not require a model, so it works well for complex systems with underlying mechanisms t
From playlist Fuzzy Logic
Introduction to Fuzzy Logic, Fuzzy Logic System, Fuzzy Logic Controller
This video is about the introduction of Fuzzy Logic System which is also referred as Fuzzy Inference System. The basic concept of fuzzy sets and the working principle of a Fuzzy Logic System (Fuzzy Inference System) will be described. A fuzzy controller implemented by a Fuzzy Logic System
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Fuzzy Logic Systems - Part 1: Introduction
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Fuzzy Logic Examples | Fuzzy Logic Part 3
Watch this fuzzy logic example of a fuzzy inference system that can balance a pole on a cart. You can design a fuzzy logic controller using just experience and intuition about the system—no mathematical models necessary. Fuzzy Logic Toolbox: https://bit.ly/3kypWT4?s_eid=PSM_15028 -------
From playlist Fuzzy Logic
Fuzzy Logic Systems - Part 2: Fuzzy Inference System
This video is about Fuzzy Logic Systems - Part 2: Fuzzy Inference System
From playlist Fuzzy Logic
How to create a fuzzy inference system
Learn how to graphically design and simulate fuzzy inference systems using the fuzzy logic designer app. The video demonstrates the steps to create a fuzzy logic to estimate the tip percentage for a waiter based on the quality of food and service. - Build fuzzy inference systems and fuzz
From playlist “How To” with MATLAB and Simulink
Fuzzy Logic Controller Tuning | Fuzzy Logic, Part 4
Cover the basics of data-driven approaches to fuzzy logic controller tuning and fuzzy inference systems. See how to tune fuzzy inference parameters to find optimal solutions. Learn how optimization algorithms, like genetic algorithms and pattern search, can efficiently tune the parameters
From playlist Fuzzy Logic
Fuzzy Inference System Walkthrough | Fuzzy Logic, Part 2
This video walks step-by-step through a fuzzy inference system. Learn concepts like membership function shapes, fuzzy operators, multiple-input inference systems, and rule firing strength. Fuzzy Logic Toolbox: https://bit.ly/38xNy7E?s_eid=PSM_15028 ---------------------------------------
From playlist Fuzzy Logic
WSU: Fundamental Lessons from String Theory with Cumrun Vafa
Cumrun Vafa, together with fellow world-renowned string theorist Andrew Strominger, developed a new way to calculate black hole entropy in the language of string theory. Follow Vafa as he guides you through some of the more incredible things we have learned since string theory’s inception.
From playlist WSU Master Classes
WSU: Fundamental Lessons from String Theory with Cumrun Vafa
Cumrun Vafa, together with fellow world-renowned string theorist Andrew Strominger, developed a new way to calculate black hole entropy in the language of string theory. Follow Vafa as he guides you through some of the more incredible things we have learned since string theory’s inception.
From playlist WSU Master Class
Interval Type-2 (IT2) Fuzzy System and its Applications
Abstract: This talk will be delivered in two parts while the first part is a brief introduction of fuzzy logic systems from the control point of view while the second part is about the fuzzy-logic related applications. In the first part, the fuzzy logic system will be introduced and its fu
From playlist Fuzzy Logic
22C3: A way to fuzzy democracy
Speakers: Svenja Schröder, Christiane Ruetten Using modern communication to transform the way we make political decisions As we can see by the German voting results in 2005, there is a huge disenchantment with politics in modern democracies. The voting people feel powerless in a governan
From playlist 22C3: Private Investigations
From playlist Week 1 2015 Shorts
Evolutionary Approach to Clustering by Ujjwal Maulik
Program Summer Research Program on Dynamics of Complex Systems ORGANIZERS: Amit Apte, Soumitro Banerjee, Pranay Goel, Partha Guha, Neelima Gupte, Govindan Rangarajan and Somdatta Sinha DATE : 15 May 2019 to 12 July 2019 VENUE : Madhava hall for Summer School & Ramanujan hall f
From playlist Summer Research Program On Dynamics Of Complex Systems 2019
Pawel Grzegrzolka - Asymptotic dimension of fuzzy metric spaces
38th Annual Geometric Topology Workshop (Online), June 15-17, 2021 Pawel Grzegrzolka, Stanford University Title: Asymptotic dimension of fuzzy metric spaces Abstract: In this talk, we will discuss asymptotic dimension of fuzzy metric spaces. After a short introduction to fuzzy metric spac
From playlist 38th Annual Geometric Topology Workshop (Online), June 15-17, 2021
Measuring a golden statue | Measurement and data | Early Math | Khan Academy
Courses on Khan Academy are always 100% free. Start practicing—and saving your progress—now: https://www.khanacademy.org/math/cc-1st-grade-math/cc-1st-measurement-geometry/copy-of-cc-early-math-length-intro/v/basic-measurement Measure an object with same-size length units that span it wi
From playlist Measurement and data | 1st Grade | Khan Academy
Fuzzy Logic Systems - Part 4: Knowledge Based and Fuzzy Inference Engine
This video is about Fuzzy Logic Systems - Part 4: Knowledge Based and Fuzzy Inference Engine
From playlist Fuzzy Logic