Fuzzy rules are used within fuzzy logic systems to infer an output based on input variables. Modus ponens and modus tollens are the most important rules of inference. A modus ponens rule is in the form Premise: x is AImplication: IF x is A THEN y is BConsequent: y is B In crisp logic, the premise x is A can only be true or false. However, in a fuzzy rule, the premise x is A and the consequent y is B can be true to a degree, instead of entirely true or entirely false. This is achieved by representing the linguistic variables A and B using fuzzy sets. In a fuzzy rule, modus ponens is extended to generalised modus ponens:. Premise: x is A*Implication: IF x is A THEN y is BConsequent: y is B* The key difference is that the premise x is A can be only partially true. As a result, the consequent y is B is also partially true. Truth is represented as a real number between 0 and 1, where 0 is false and 1 is true. (Wikipedia).
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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
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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
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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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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
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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
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From playlist “How To” with MATLAB and Simulink
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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
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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 3)
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Simulate and analyze fuzzy inference systems. For more videos, visit http://www.mathworks.com/products/fuzzy-logic/examples.html
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From playlist Demonstrations