Logic in computer science | Fuzzy logic
Type-2 fuzzy sets and systems generalize standard Type-1 fuzzy sets and systems so that more uncertainty can be handled. From the beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something that seems to contradict the word fuzzy, since that word has the connotation of much uncertainty. So, what does one do when there is uncertainty about the value of the membership function? The answer to this question was provided in 1975 by the inventor of fuzzy sets, Lotfi A. Zadeh, when he proposed more sophisticated kinds of fuzzy sets, the first of which he called a "type-2 fuzzy set". A type-2 fuzzy set lets us incorporate uncertainty about the membership function into fuzzy set theory, and is a way to address the above criticism of type-1 fuzzy sets head-on. And, if there is no uncertainty, then a type-2 fuzzy set reduces to a type-1 fuzzy set, which is analogous to probability reducing to determinism when unpredictability vanishes. Type1 fuzzy systems are working with a fixed membership function, while in type-2 fuzzy systems the membership function is fluctuating. A fuzzy set determines how input values are converted into fuzzy variables. (Wikipedia).
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
Fuzzy Logic Systems - Part 2: Fuzzy Inference System
This video is about Fuzzy Logic Systems - Part 2: Fuzzy Inference System
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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Introduction to sets || Set theory Overview - Part 2
A set is the mathematical model for a collection of different things; a set contains elements or members, which can be mathematical objects of any kind: numbers, symbols, points in space, lines, other geometrical shapes, variables, or even other #sets. The #set with no element is the empty
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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
Fuzzy Logic Systems - Part 6: Three Fuzzy Inference Systems
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From playlist Fuzzy Logic
Introduction to sets || Set theory Overview - Part 1
A set is the mathematical model for a collection of different things; a set contains elements or members, which can be mathematical objects of any kind: numbers, symbols, points in space, lines, other geometrical shapes, variables, or even other #sets. The #set with no element is the empty
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https://indico.math.cnrs.fr/event/4272/attachments/2260/2719/IHESConference_Denjoe_OCONNOR.pdf
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BlueHat v9: Office Security Engineering 2/5
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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
[BOURBAKI 2018] 31/03/2018 - 1/3 - Gabriel RIVIÈRE
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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
Artificial Pancreas Control Using Fuzzy Logic
Design an artificial pancreas nonlinear control system in Simulink® using fuzzy logic. Design a complex fuzzy logic controller by combining two smaller interconnected fuzzy systems in a fuzzy tree. Automatically tune the membership function parameters and rules of a fuzzy inference system.
From playlist AI, Machine Learning, Data Science | Developer Tech Showcase
Getting Started with Fuzzy Logic Toolbox (Part 2)
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Define membership functions and rules for fuzzy inference systems. For more videos, visit http://www.mathworks.com/products/fuzzy-logic/examples.html
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