• documentation
main content

fuzzy inference system tuning -凯发k8网页登录

tune membership functions and rules of fuzzy systems

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 .

if your system is a single-output type-1 sugeno fis, you can tune its membership function parameters using neuro-adaptive learning methods. this tuning method does not require global optimization toolbox software. for more information, see .

apps

fuzzy logic designerdesign, test, and tune fuzzy inference systems

functions

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
tune sugeno-type fuzzy inference system using training data
option set for anfis function

objects

tunable parameter settings of fuzzy rules
tunable parameter settings of fuzzy variables
tunable parameter settings for fuzzy membership functions
tunable parameter settings for type-2 fuzzy membership functions
parameter settings for rule clauses
tunable numeric parameter settings of membership functions

topics

tune fuzzy systems


  • tune fuzzy membership function parameters and learn new fuzzy rules.

  • interactively learn rules and tune membership function parameters of a fuzzy inference system.


  • programmatically learn rules and tune membership function parameters of a fuzzy inference system.


  • you can tune the learn rules and tune membership function parameters for fiss within a fuzzy tree.

  • you can customize the fis tuning process by specifying either a custom cost function or a custom optimization method.

  • to prevent overfitting during fis parameter optimization, you can stop the tuning process early based on an unbiased evaluation of the model using validation data.

  • tune the rules and membership function parameters for a tree of interconnected sugeno fuzzy systems.

  • tune the rules and membership function parameters for a fis with type-2 membership functions.

  • when you do not have training data, you can tune your fuzzy system using a custom cost function that simulates the fis operation.

train anfis systems


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

  • interactively create, train, and test neuro-fuzzy systems using the fuzzy logic designer app.

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

  • perform adaptive nonlinear noise cancellation using the anfis and genfis commands.

  • generate a fuzzy inference system from data using subtractive clustering.

  • predict fuel consumption for automobiles using an adaptive neuro-fuzzy inference system and previously recorded observations.

  • you can model nonlinear dynamic system behavior using adaptive neuro-fuzzy systems.
网站地图