You can tune the membership function parameters and rules of your fuzzy inference system using Global Optimization Toolbox tuning methods such as genetic algorithms and particle swarm optimization. For more information, see Tuning Fuzzy Inference Systems.
If your system is a single-output Sugeno FIS, you can tune its membership function parameters you neuro-adaptive learning methods. This tuning method does not require Global Optimization Toolboxsoftware. For more information, see Neuro-Adaptive Learning and ANFIS.
|Neuro-Fuzzy Designer||Design, train, and test Sugeno-type fuzzy inference systems|
|Tune fuzzy inference system or tree of fuzzy inference systems|
|Option set for tunefis function|
|Obtain tunable settings from fuzzy inference system|
|Set specified parameter settings as tunable or nontunable|
|Obtain values of tunable parameters from fuzzy inference system|
|Specify tunable parameter values of a fuzzy inference system|
|Tunable parameter settings of fuzzy rules|
|Tunable parameter settings of fuzzy variables|
|Tunable parameter settings of fuzzy membership functions|
|Parameter settings for rule clauses|
|Tunable numeric parameter settings of membership functions|
Tune fuzzy membership function parameters and learn new fuzzy rules.
Learn rules and tune membership function parameters for a Mamdani fuzzy system.
Tune the rules and membership function parameters for a tree of interconnected Sugeno fuzzy systems.
When you do not have training data, you can tune your fuzzy system using a custom cost function that simulates the FIS operation.
You can tune Sugeno fuzzy inference systems using neuro-adaptive learning techniques similar to those used for training neural networks.
You can design neuro-fuzzy systems either at the command line or using the Neuro-Fuzzy Designer app.
Interactively create, train, and test neuro-fuzzy systems using the Neuro-Fuzzy Designer app.
Validate trained neuro-fuzzy systems using checking data that is different from training data.
When using Neuro-Fuzzy Designer, you can export your trained neuro-fuzzy model and training error data to the MATLAB® workspace for further analysis.
Train a neuro-fuzzy system for time-series prediction using the
Determine joint angles required to place the tip of a robotic arm in a desired location using a neuro-fuzzy model.
Perform adaptive nonlinear noise cancellation using the
This example shows how to predict of fuel consumption (miles per gallon) for automobiles, using data from previously recorded observations.
You can model nonlinear dynamic system behavior using adaptive neuro-fuzzy systems.