# Adaptive neuro fuzzy inference system

An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered to be a universal estimator. For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm. It has uses in intelligent situational aware energy management system. (Wikipedia).

Fuzzy Logic Systems - Part 2: Fuzzy Inference System

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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

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Fuzzy Logic Systems - Part 1: Introduction

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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

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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

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Fuzzy Logic Systems - Part 4: Knowledge Based and Fuzzy Inference Engine

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Darwin's Legacy | Lecture 9

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

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

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 -------

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