anfisOptions

Option set for anfis command

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Syntax

opt = anfisOptions
opt = anfisOptions(Name,Value)

Description

example

opt = anfisOptions creates a default option set for tuning a Sugeno fuzzy inference system using anfis. Use dot notation to modify this option set for your specific application. Any options that you do not modify retain their default values.

example

opt = anfisOptions(Name,Value) creates an option set with options specified by one or more Name,Value pair arguments.

Examples

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Open Live Script

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 pair arguments.

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

Input Arguments

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Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'EpochNumber',50 sets the maximum number of training epochs to 50.

Initial FIS structure to tune, specified as the comma-separated pair consisting of 'InitialFIS' and one of the following:

  • 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 genfis command 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'.

    • No rule sharing. Different rules cannot use the same output membership function; that is, the number of output membership functions must equal the number of rules.

    • Unity weight for each rule.

    • No custom membership functions or defuzzification methods.

Maximum number of training epochs, specified as the comma-separated pair consisting of 'EpochNumber' and a positive integer. The training process stops when it reaches the maximum number of training epochs.

Training error goal, specified as the comma-separated pair consisting of 'ErrorGoal' and a scalar. The training process stops when the training error is less than or equal to ErrorGoal.

Initial training step size, specified as the comma-separated pair consisting of 'InitialStepSize' and 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).

Step-size decrease rate, specified as the comma-separated pair consisting of 'StepSizeDecreaseRate' and 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.

Step-size increase rate, specified as the comma-separated pair consisting of 'StepSizeIncreaseRate' and a scalar greater than 1. If the training error decreases for four consecutive epochs, then anfis scales the step size by the increase rate.

Flag for showing ANFIS information at the start of the training process, specified as the comma-separated pair consisting of 'DisplayANFISInformation' and one of the following:

  • 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.

Flag for showing training error values after each training epoch, specified as the comma-separated pair consisting of 'DisplayErrorValues' and one of the following:

  • 1 — Display the training error.

  • 0 — Do not display the training error.

Flag for showing step size whenever the step size changes, specified as the comma-separated pair consisting of 'DisplayStepSize' and one of the following:

  • 1 — Display the step size.

  • 0 — Do not display the step size.

Flag for displaying final results after training, specified as the comma-separated pair consisting of 'DisplayFinalResults' and one of the following:

  • 1 — Display the results.

  • 0 — Do not display the results.

Validation data for preventing overfitting to the training data, specified as the comma-separated pair consisting of 'ValidationData' and 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.

Optimization method used in membership function parameter training, specified as the comma-separated pair consisting of 'OptimizationMethod' and one of the following:

  • 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

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Training options for anfis command, returned as an anfisOptions option set.

See Also

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Introduced in R2017a

Fuzzy Logic Toolbox Documentation

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