[___] = getTunableSettings(fis,'AsymmetricLag',true)
returns tunable settings which allow asymmetric lower membership function lag values. This
syntax is supported only when fis is a type-2 fuzzy inference
system.
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.
Fuzzy inference system, specified as a mamfis,
sugfis,
mamfistype2,
sugfistype2, or
fistree object. The fuzzy system can be a
fuzzy inference system or network of interconnected fuzzy inference systems.
in — Tunable settings of input variables array of VariableSettings objects
Tunable settings for input variables, returned as an array of VariableSettings objects. Each
VariableSettings object contains
tunability settings for the input variable indicated by its FISName
and VariableName properties.
Specify the tunability settings of the membership functions for this variable, using
its MembershipFunctions property.
out — Tunable settings of output variables array of VariableSettings objects
Tunable settings for input variables, returned as an array of VariableSettings objects. Each
VariableSettings object contains
tunability settings for the output variable indicated by its FISName
and VariableName properties.
Specify the tunability settings of the membership functions for this variable, using
its MembershipFunctions property.
rule — Tunable settings of rules array of RuleSettings objects
Tunable settings for rules, returned as an array of RuleSettings object. Each
RuleSettings object contain tunability settings for a rule from the
FIS indicated by its FISName property.
Specify the tunability settings of the antecedent and consequent for this variable,
using its Antecedent and Consequent properties,
respectively.
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