anfisOptions

(Not recommended) ANFIS tuning options

anfisOptions is not recommended. When tuning a fuzzy inference system using tunefis, specify options using the MethodOptions property of a tunefisOptions object. For more information, see Not recommended.

Syntax

opt = anfisOptions

opt = anfisOptions(Name=Value)

Description

opt = anfisOptions returns a default option set for tuning a Sugeno fuzzy inference system using anfis. To modify options, use dot notation.

opt = anfisOptions(Name=Value) specifies options using one or more name-value arguments. For example, opt = anfisOptions(EpochNumber=50) sets the number of training epochs to 50.

Examples

Create Option Set for ANFIS Training

Create a default option set.

opt = anfisOptions;

Specify training options using dot notation. For example, specify the following options:

• Initial FIS with 4 membership functions for each input variable

• Maximum number of training epochs equal to 30.

opt.InitialFIS = 4;
opt.EpochNumber = 30;

You can also specify options when creating the option set using one or more name-value arguments.

opt2 = anfisOptions(InitialFIS=4,EpochNumber=30);

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: opt = anfisOptions(EpochNumber=50) sets the number of training epochs to 50.

InitialFIS — Initial FIS structure

2 (default) | positive integer greater than 1 | vector of positive integers | FIS structure

Initial FIS structure to tune, specified as one of the following values.

• Positive integer greater than 1 specifying the number of membership functions for all input variables. anfis generates an initial FIS structure with the specified number of membership functions using genfis with grid partitioning.

• Vector of positive integers with length equal to the number of input variables specifying the number of membership functions for each input variable. anfis generates an initial FIS structure with the specified numbers of membership functions using genfis with grid partitioning.

• FIS structure generated using the genfis function with grid partitioning or subtractive clustering. The specified system must have the following properties:

  • Single output, obtained using weighted average defuzzification.

  • First or zeroth order Sugeno-type system; that is, all output membership functions must be the same type and be either "linear" or "constant".

  • The number of output membership functions must equal the number of rules and each rule must use a different output membership function.

  • Unity weight for each rule.

  • No custom membership functions or defuzzification methods.

EpochNumber — Maximum number of training epochs

10 (default) | positive integer

Maximum number of training epochs, specified as a positive integer. The training process stops when it reaches the maximum number of training epochs.

ErrorGoal — Training error goal

0 (default) | scalar

Training error goal, specified as a scalar. The training process stops when the training error is less than or equal to ErrorGoal.

InitialStepSize — Initial training step size

0.01 (default) | positive scalar

Initial training step size, specified as a positive scalar.

The anfis training algorithm tunes the FIS parameters using gradient descent optimization methods. The training step size is the magnitude of each gradient transition in the parameter space. Typically, you can increase the rate of convergence of the training algorithm by increasing the step size. During optimization, anfis automatically updates the step size using StepSizeIncreaseRate and StepSizeDecreaseRate.

Generally, the step-size profile during training is a curve that increases initially, reaches some maximum, and then decreases for the remainder of the training. To achieve this ideal step-size profile, adjust the initial step-size and the increase and decrease rates (opt.StepSizeDecreaseRate, opt.StepSizeIncreaseRate).

StepSizeDecreaseRate — Step-size decrease rate

0.9 (default) | positive scalar less than 1

Step-size decrease rate, specified as a positive scalar less than 1. If the training error undergoes two consecutive combinations of an increase followed by a decrease, then anfis scales the step size by the decrease rate.

StepSizeIncreaseRate — Step-size increase rate

1.1 (default) | scalar greater than 1

Step-size increase rate, specified as a scalar greater than 1. If the training error decreases for four consecutive epochs, then anfis scales the step size by the increase rate.

DisplayANFISInformation — Flag for showing ANFIS information

1 (default) | 0

Flag for showing ANFIS information at the start of the training process, specified as one of the following values.

• 1 — Display the following information about the ANFIS system and training data:

  • Number of nodes in the ANFIS system

  • Number of linear parameters to tune

  • Number of nonlinear parameters to tune

  • Total number of parameters to tune

  • Number of training data pairs

  • Number of checking data pairs

  • Number of fuzzy rules

• 0 — Do not display the information.

DisplayErrorValues — Flag for showing training error values

1 (default) | 0

Flag for showing training error values after each training epoch, specified as one of the following values.

• 1 — Display the training error.

• 0 — Do not display the training error.

DisplayStepSize — Flag for showing step size

1 (default) | 0

Flag for showing step size whenever the step size changes, specified as one of the following values.

• 1 — Display the step size.

• 0 — Do not display the step size.

DisplayFinalResults — Flag for displaying final results

1 (default) | 0

Flag for displaying final results after training, specified as one of the following values.

• 1 — Display the results.

• 0 — Do not display the results.

ValidationData — Validation data

[] (default) | array

Validation data for preventing overfitting to the training data, specified as an array. For a fuzzy system with N inputs, specify ValidationData as an array with N+1 columns. The first N columns contain input data and the final column contains output data. Each row of ValidationData contains one data point.

At each training epoch, the training algorithm validates the FIS using the validation data.

Generally, validation data should fully represent the features of the data the FIS is intended to model, while also being sufficiently different from the training data to test training generalization.

OptimizationMethod — Optimization method

1 (default) | 0

Optimization method used in membership function parameter training, specified as one of the following values.

• 1 — Use a hybrid method, which uses a combination of backpropagation to compute input membership function parameters, and least squares estimation to compute output membership function parameters.

• 0 — Use backpropagation gradient descent to compute all parameters.

Output Arguments

opt — Options for ANFIS training

ANFIS training options object

Options for ANFIS training, returned as an ANFIS training options object.

Version History

Introduced in R2017a

R2026a: Not recommended

anfisOptions is not recommended. When tuning a fuzzy inference system using tunefis, specify options using the MethodOptions property of a tunefisOptions object.

This table shows some typical usages of anfis and anfisOptions and how to update your code to use tunefis and tunefisOptions.

Not recommended	Recommended
opt = anfisOptions(EpochNumber=40); fis = anfis(trnData,opt); where the first two columns of trnData contain input values and the third column of trnData contains output data.	inFIS = genfis(trnData(:,1:2),trnData(:,3)); opt = tunefisOptions(Method="anfis"); opt.MethodOptions.EpochNumber = 40; fis = tunefis(inFIS,[], ... trnData(:,1:2),trnData(:,3),opt);
opt = anfisOptions(InitialFIS=4); fis = anfis([trnX trnY],opt);	genOpt = genfisOptions("GridPartition"); genOpt.NumMembershipFunctions = 4; inFIS = genfis(trnX,trnY,genOpt); opt = tunefisOptions(Method="anfis"); fis = tunefis(inFIS,[],trnX,trnY,opt);
opt = anfisOptions(ValidationData=[valX valY]); [trnFIS,trnError,stepSize,valFIS,valError] = ... anfis([trnX trnY],opt);	inFIS = genfis(trnX,trnY); opt = tunefisOptions(Method="anfis"); opt.MethodOptions.ValidationData = [valX valY]; [trnFIS,summary] = tunefis(inFIS,[], ... trnX,trnY,opt); trnError = summary.tuningOutputs.trainError; stepSize = summary.tuningOutputs.stepSize; valFIS = summary.tuningOutputs.chkFIS; valError = summary.tuningOutputs.chkError;