Documentation

This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Adaptive Neuro-Fuzzy Modeling

Build Adaptive Neuro-Fuzzy Inference Systems (ANFIS), train Sugeno systems using neuro-adaptive learning

An adaptive neuro-fuzzy inference system (ANFIS) is a fuzzy system whose membership function parameters have been tuned using neuro-adaptive learning methods similar to those used in training neural networks. Fuzzy Logic Toolbox™ software provides command-line functions and an app for training Sugeno-type fuzzy inference systems using given input/output training data. For more information see Neuro-Adaptive Learning and ANFIS.

Apps

Neuro-Fuzzy Designer Design, train, and test Sugeno-type fuzzy inference systems

Functions

newfis Create new fuzzy inference system
genfis Generate fuzzy inference system structure from data
genfisOptions Option set for genfis command
mam2sug Transform Mamdani Fuzzy Inference System into Sugeno Fuzzy Inference System
anfis Tune Sugeno-type fuzzy inference system using training data
anfisOptions Option set for anfis command
evalfis Perform fuzzy inference calculations
gensurf Generate fuzzy inference system output surface
gensurfOptions Option set for gensurf command
surfview Open Surface Viewer
plotfis Plot Fuzzy Inference System
showfis Display annotated Fuzzy Inference System
readfis Load Fuzzy Inference System from file
writefis Save fuzzy inference system to file

Topics

ANFIS Basics

Fuzzy Inference Process

Fuzzy inference maps an input space to an output space using a series of fuzzy if-then rules.

What Is Sugeno-Type Fuzzy Inference?

In Sugeno systems, the output of each if-then rule is either constant or a linear function of the input variables. The final output value is the weighted average of all rule outputs.

Neuro-Adaptive Learning and ANFIS

You can tune Sugeno fuzzy inference systems using neuro-adaptive learning techniques similar to those used for training neural networks.

Comparison of anfis and Neuro-Fuzzy Designer Functionality

You can design neuro-fuzzy systems either at the command line or using the Neuro-Fuzzy Designer app.

Train and Test ANFIS

Train Adaptive Neuro-Fuzzy Inference Systems

Interactively create, train, and test neuro-fuzzy systems using the Neuro-Fuzzy Designer app.

Test Data Against Trained System

Validate trained neuro-fuzzy systems using checking data that is different from training data.

Save Training Error Data to MATLAB Workspace

When using Neuro-Fuzzy Designer, you can export your trained neuro-fuzzy model and training error data to the MATLAB® workspace for further analysis.

Case Studies

Predict Chaotic Time-Series

Train a neuro-fuzzy system for time-series prediction using the anfis command.

Modeling Inverse Kinematics in a Robotic Arm

Determine the joint angles required to place the tip of a robotic arm in a desired location using a neuro-fuzzy model.

Adaptive Noise Cancellation Using ANFIS

Perform adaptive nonlinear noise cancellation using the anfis and genfis commands.

Was this topic helpful?