tunefisOptions
Option set for tunefis
function
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
Description
creates a default
option set for tuning a fuzzy inference system using the opt
= tunefisOptionstunefis
function. To modify the properties of this option set for your
specific application, use dot notation.
creates an option set with Properties specified using one or more
name-value pair arguments.opt
= tunefisOptions(Name,Value
)
Properties
Method
— Tuning algorithm
"ga"
(default) | "particleswarm"
| "patternsearch"
| "simulannealbnd"
| "anfis"
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.
MethodOptions
— Tuning algorithm options
options created using optimoptions
| anfisOptions
object
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.
OptimizationType
— Type of optimization
"tuning"
(default) | "learning"
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 byNumMaxRules
.
The "anfis"
algorithm supports only "tuning"
optimization.
NumMaxRules
— Maximum number of rules in a FIS
Inf
(default) | integer
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.
IgnoreInvalidParameters
— Flag for ignoring invalid parameters
true
(default) | false
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.
DistanceMetric
— Type of distance metric
"rmse"
(default) | "norm1"
| "norm2"
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.
UseParallel
— Flag for using parallel computing
false
(default) | true
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.
KFoldValue
— Number of cross validations to perform
0
(default) | nonnegative integer
Since R2020a
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 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.
ValidationTolerance
— Maximum allowable increase in validation cost
0.1
(default) | value in the range [0,1]
Since R2020a
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.
ValidationWindowSize
— Window size for computing average validation cost
5
(default) | positive integer
Since R2020a
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.
Display
— Data to display in command window during training
"all"
(default) | "tuningonly"
| "validationonly"
| "none"
Since R2020a
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
Specify Options for FIS Tuning
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"
Version History
Introduced in R2019a
See Also
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