Main Content

tunefisOptions

Option set for tunefis function

expand all in page

Description

Use a tunefisOptions object to specify options for tuning fuzzy systems using the tunefis function. You can specify options such as the optimization method, optimization type, and distance metric for optimization cost calculation.

Creation

Syntax

opt = tunefisOptions
opt = tunefisOptions(Name,Value)

Description

example

opt = tunefisOptions creates a default option set for tuning a fuzzy inference system using the tunefis function. To modify the properties of this option set for your specific application, use dot notation.

example

opt = tunefisOptions(Name,Value) creates an option set with Properties specified using one or more name-value pair arguments.

Properties

expand all

Tuning algorithm, specified as one of the following:

  • "ga" — genetic algorithm

  • "particleswarm" — particle swarm

  • "patternsearch" — pattern search

  • "simulannealbnd" — simulated annealing algorithm

  • "anfis" — adaptive neuro-fuzzy

These tuning algorithms use solvers from the Global Optimization Toolbox software, except for "anfis". The MethodOptions property differs for each algorithm, and corresponds to the options input argument for the respective solver. If you specify MethodOptions without specifying Method, then the tuning method is determined based on MethodOptions.

The "anfis" tuning method supports tuning only type-1 Sugeno fuzzy inference systems with one output variable.

Tuning algorithm options, specified as an option object for the tuning algorithm specified by Method. This property differs for each algorithm and is created using optimoptions. If you do not specify MethodOptions, tunefis creates a default option object for the tuning method specified in Method. To modify the options in MethodOptions, use dot notation.

Type of optimization, specified as one of the following:

  • "tuning" — Optimize the existing input, output, and rule parameters without learning new rules.

  • "learning" — Learn new rules up to the maximum number of rules specified by NumMaxRules.

The "anfis" algorithm supports only "tuning" optimization.

Maximum number of rules in a FIS after optimization, specified as an integer. The number of rules in a FIS (after optimization) can be less than NumMaxRules, since duplicate rules with the same antecedent values are removed from the rule base.

When NumMaxRules is Inf, tunefis sets NumMaxRules to the maximum number of possible rules for the FIS. This maximum value is computed based on the number of input variables and the number of membership functions for each input variable.

When tuning the parameters of a fistree object, NumMaxRules indicates the maximum number of rules for each FIS in the fistree.

The "anfis" tuning method ignores this option.

Flag for ignoring invalid parameters, specified as either true or false. When IgnoreInvalideParameters is true, the tunefis function ignores invalid parameter values generated during the tuning process.

The "anfis" tuning method ignores this option.

Type of distance metric used for computing the cost for the optimized parameter values with respect to the training data, specified as one of the following:

  • "rmse" — Root-mean-squared error

  • "norm1" — Vector 1-norm

  • "norm2" — Vector 2-norm

For more information on vector norms, see norm.

The "anfis" tuning method supports only the "rmse" metric.

Flag for using parallel computing, specified as either true or false. When UseParallel is true, the tunefis function uses parallel computation in the optimization process. Using parallel computing requires Parallel Computing Toolbox™ software.

The "anfis" tuning method does not support parallel computation.

Number of cross validations to perform, specified as a nonnegative integer less than or equal to the number of rows in the training data.

When KFoldValue is 0 or 1, tunefis uses the entire input data set for training and does not perform validation.

Otherwise, tunefis randomly partitions the input data into KFoldValue subsets of approximately equal size. The function then performs KFoldValue training-validation iterations. For each iteration, one data subset is used as validation data with the remaining subsets used as training data. The following figure shows the data partition and iterations for KFoldValue = 4.

For each of the four training-validation iterations, a different subset of the training data is selected as validation data with the remaining data used for training.

For an example that tunes a fuzzy inference system using k-fold cross validation, see Optimize FIS Parameters with k-Fold Cross-Validation.

The "anfis" tuning method ignores this option.

Maximum allowable increase in validation cost when using k-fold cross validation, specified as a scalar value in the range [0,1]. A higher ValidationTolerance value produces a longer training-validation iteration, with an increased possibility of data overfitting.

The increase in validation cost, ΔC, is the difference between the average validation cost and the minimum validation cost, Cmin, for the current training-validation iteration. The average validation cost is a moving average with a window size equal to ValidationWindowSize.

tunefis stops the current training-validation iteration when the ratio between ΔC and Cmin exceeds ValidationTolerance.

ValidationTolerance is ignored when KFoldValue is 0 or 1.

The "anfis" tuning method ignores this option.

Window size for computing average validation cost, specified as a positive integer. The validation cost moving average is computed over the last N validation cost values, where N is equal to ValidationWindowSize. A higher ValidationWindowSize value produces a longer training-validation iteration, with an increased possibility of data overfitting. A lower window size can cause early termination of the tuning process when the training data is noisy.

ValidationWindowSize is ignored when KFoldValue is 0 or 1.

The "anfis" tuning method ignores this option.

Data to display in command window during training, specified as one of the following values.

  • "all" — Display both training and validation results.

  • "tuningonly" — Display only training results.

  • "validationonly" — Display only validation results.

  • "none" — Display neither training nor validation results.

Examples

collapse all

Open Live Script

Create a default option set using the particle swarm tuning algorithm.

opt = tunefisOptions("Method","particleswarm")
opt = 
  tunefisOptions with properties:

                     Method: "particleswarm"
              MethodOptions: [1x1 optim.options.Particleswarm]
           OptimizationType: "tuning"
                NumMaxRules: Inf
    IgnoreInvalidParameters: 1
             DistanceMetric: "rmse"
                UseParallel: 0
                 KFoldValue: 0
        ValidationTolerance: 0.1000
       ValidationWindowSize: 5
                    Display: "all"

You can modify the options using dot notation. For example, set the maximum number of iterations to 20.

opt.MethodOptions.MaxIterations = 20;

You can also specify other options when creating the option set. In this example, set the OptimizationType to "learning" to learn new rules.

opt2 = tunefisOptions("Method","particleswarm","OptimizationType","learning")
opt2 = 
  tunefisOptions with properties:

                     Method: "particleswarm"
              MethodOptions: [1x1 optim.options.Particleswarm]
           OptimizationType: "learning"
                NumMaxRules: Inf
    IgnoreInvalidParameters: 1
             DistanceMetric: "rmse"
                UseParallel: 0
                 KFoldValue: 0
        ValidationTolerance: 0.1000
       ValidationWindowSize: 5
                    Display: "all"

See Also

|

Introduced in R2019a

Fuzzy Logic Toolbox Documentation

Support

Free Ebook: Managing Model-Based Design

Free Ebook: Managing Model-Based Design

Download now