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
From playlist Fuzzy Logic
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 Logic Systems - Part 1: Introduction
This video is about Fuzzy Logic Systems - Part 1: Introduction
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
Fuzzy Logic Systems - Part 6: Three Fuzzy Inference Systems
This video is about Fuzzy Logic Systems - Part 6: Three Fuzzy Inference Systems
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
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
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
November 17, 2008 lecture by Russell Fernald for the Stanford Continuing Studies course on Darwin's Legacy (DAR 200). Dr. Fernald discusses how social behavior changes the brains of fish, animals, and humans to adapt to situations typically involving mating behaviors. The lecture is conclu
From playlist Lecture Collection | Darwin's Legacy
Isabelle Bloch - Hybrid AI for Knowledge Representation and Model-based Image Understanding - (...)
This presentation will focus on hybrid AI, as a step towards explainability, more specifically in the domain of spatial reasoning and image understanding. Image understanding benefits from the modeling of knowledge about both the scene observed and the objects it contains as well as their
From playlist 8th edition of the Statistics & Computer Science Day for Data Science in Paris-Saclay, 9 March 2023
End-to-End Differentiable Proving: Tim Rocktäschel, University of Oxford
We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specific
From playlist Logic and learning workshop
Dynamic Inference with Neural Interpreters (w/ author interview)
#deeplearning #neuralinterpreter #ai This video includes an interview with the paper's authors! What if we treated deep networks like modular programs? Neural Interpreters divide computation into small modules and route data to them via a dynamic type inference system. The resulting model
From playlist Papers Explained
Latent Stochastic Differential Equations for Irregularly-Sampled Time Series - David Duvenaud
Seminar on Theoretical Machine Learning Topic: Latent Stochastic Differential Equations for Irregularly-Sampled Time Series Speaker: David Duvenaud Affiliation: University of Toronto Date: April 30, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Stanford Seminar - The future of low power circuits and embedded intelligence
Speaker: Edith Beigné, CEA France Circuit and design division at CEA LETI is focusing on innovative architectures and circuits dedicated to digital, imagers, wireless, sensors, power management and embedded software. After a brief overview of adaptive circuits for low power multi-process
From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series
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