getTunableSettings

Create a fuzzy inference system.

fis1 = mamfis('Name','fis1','NumInputs',2,'NumOutputs',1);

Obtain the tunable settings of the inputs, outputs, and rules of the fuzzy inference system.

[in,out,rule] = getTunableSettings(fis1);

You can use dot notation to specify tunable settings.

For the first membership function of input 1:

in(1).MembershipFunctions(1).Parameters.Free(1) = false;
in(1).MembershipFunctions(1).Parameters.Minimum(2:end) = 0;
in(1).MembershipFunctions(1).Parameters.Maximum(2:end) = 1;

For the first rule:

rule(1).Antecedent.Free(1) = false;
rule(1).Antecedent.AllowNot(2) = true;
rule(1).Consequent.AllowEmpty(1) = false;

Create a fuzzy inference system.

fis1 = mamfis('Name','fis1','NumInputs',2,'NumOutputs',1);

Obtain the tunable settings of input and output variables of the fuzzy inference system.

[in,out] = getTunableSettings(fis1);

You can use dot notation to specify the tunable settings of input and output variables.

For the first membership function of input 1, set the first and third parameters to tunable.

in(1).MembershipFunctions(1).Parameters.Free = [1 0 1];

For the first membership function of input 2, set the minimum parameter range to 0.

in(2).MembershipFunctions(1).Parameters.Minimum = 0;

For the first membership function of the output variable, set the maximum parameter range to 1.

out(1).MembershipFunctions(1).Parameters.Maximum = 1;

Create a fuzzy inference system.

fis = mamfis("Name","fis1","NumInputs",2,"NumOutputs",1);

Obtain rule parameter settings from the FIS.

[~,~,rule] = getTunableSettings(fis);

Each rule parameter setting includes the FIS name, the index of the rule in the FIS, and parameter settings for the rule antecedent and consequent (the rule clauses). For each clause, you can specify the tunability settings.

View the default tunable settings for the antecedent of the first rule.

rule(1).Antecedent(1)

ans = 
  ClauseParameters with properties:

      AllowNot: [0 0]
    AllowEmpty: [1 1]
          Free: [1 1]

You can use dot notation to specify the tunable settings for each rule.

Allow NOT logic in the antecedent of rule 1.

rule(1).Antecedent.AllowNot = true;

Make the consequent of rule 2 not available for tuning.

rule(2).Consequent.Free = 0;

Do not allow absence of a variable in the consequent of rule 3.

rule(3).Consequent.AllowEmpty = false;

Create a type-2 fuzzy inference system.

fis = mamfistype2('Name','fis1','NumInputs',2,'NumOutputs',1);

Obtain the tunable settings of the input and output variables of the fuzzy inference system.

[in,out] = getTunableSettings(fis);

You can use dot notation to specify the tunable settings of the membership functions of the input and output variables.

For the first membership function of input 1, set the first and third upper membership function parameters as tunable.

in(1).MembershipFunctions(1).UpperParameters.Free = [1 0 1];

For the first membership function of input 2, set the tunable range of the lower membership function scale to be between 0.7 and 0.9.

in(2).MembershipFunctions(1).LowerScale.Minimum = 0.7;
in(2).MembershipFunctions(1).LowerScale.Maximum = 0.9;

For the first membership function of output 1, set the tunable range of the lower membership function lag to be between 0.1 and 0.4.

in(2).MembershipFunctions(1).LowerLag.Minimum = 0.1;
in(2).MembershipFunctions(1).LowerLag.Maximum = 0.4;

By default, the tunable settings for a type-2 FIS produce symmetric lag results in the tuned system. To allow for asymmetric lag results, specify the AsymmetricLag name-value argument.

[in2,out2] = getTunableSettings(fis,'AsymmetricLag',true);

Create a fuzzy inference system, and define the tunable parameter settings of inputs, outputs, and rules.

Create a FIS, and obtain its tunable settings.

fis = mamfis("NumInputs",2,"NumOutputs",2);
[in,out,rule] = getTunableSettings(fis);

You can specify all the input variables, output variables, or rules as tunable or nontunable. For example, set all the output variable settings as nontunable.

out = setTunable(out,0);

You can set the tunability of individual variables or rules. For example, set the first input variable as nontunable.

in(1) = setTunable(in(1),0);

You can set individual membership functions as nontunable. For example, set the first membership function of input 2 as nontunable.

in(2).MembershipFunctions(1) = setTunable(in(2).MembershipFunctions(1),0);

You can also specify the tunability of a subset of variables or rules. For example, set the first two rules as nontunable.

rule(1:2) = setTunable(rule(1:2),0);

Since R2023a

Create a FIS tree that contains three FIS objects.

fis1 = mamfis(Name="fis1",NumInputs=2,NumOutputs=1);
fis2 = mamfis(Name="fis2",NumInputs=2,NumOutputs=1);
fis3 = mamfis(Name="fis3",NumInputs=2,NumOutputs=1);

connections = [
    "fis1/output1" "fis3/input1";
    "fis2/output1" "fis3/input2"];

fisT = fistree([fis1 fis2 fis3],connections);

You can obtain the tunable settings for all of the FIS objects in a FIS tree or any subset of FIS objects.

Obtain the tunable settings for all FIS objects in fisT.

[in,out,rule] =  getTunableSettings(fisT);

The settings objects in, out, and rule contain variable and rule tunability settings for all FIS objects sorted alphabetically by FIS name.

Obtain the tunable settings for the variables and rules in fis3.

[in3,out3,rule3] = getTunableSettings(fisT,FIS="fis3");

You can also obtain tunable settings for multiple FIS objects in a FIS tree. Obtain the tunable settings for the rules in fis1 and fis2.

[~,~,rule12] = getTunableSettings(fisT,FIS=["fis1" "fis2"]);

When you specify tunable settings for a subset of the FIS objects in a FIS tree, the tunefis function treats the remaining FIS objects as nontunable.