The FuzzyLite Libraries for Fuzzy Logic Control¶

by Juan Rada-Vilela, PhD

The objective of the FuzzyLite Libraries is to easily design and efficiently operate fuzzy logic controllers
using object-oriented programming with minimal reliance on external libraries.

License¶

The FuzzyLite Libraries are dual-licensed under:

the GNU General Public License 3.0 and
a paid license for commercial purposes.

Please contact sales@fuzzylite.com for more information.

Repositories¶

Examples¶

FuzzyLite LanguageC++PythonJava

 # File: examples/mamdani/ObstacleAvoidance.fll
 Engine:ObstacleAvoidance
 InputVariable:obstacle
 enabled:true
 range:0.000 1.000
 lock-range:false
 term:left Ramp 1.000 0.000
 term:right Ramp 0.000 1.000
 OutputVariable:mSteer
 enabled:true
 range:0.000 1.000
 lock-range:false
 aggregation:Maximum
 defuzzifier:Centroid 100
 default:nan
 lock-previous:false
 term:left Ramp 1.000 0.000
 term:right Ramp 0.000 1.000
 RuleBlock:mamdani
 enabled:true
 conjunction:none
 disjunction:none
 implication:AlgebraicProduct
 activation:General
 rule:if obstacle is left then mSteer is right
 rule:if obstacle is right then mSteer is left

 // C++
 #include"fl/Headers.h"

 fl::Engine*engine=fl::FllImporter().fromFile("examples/mamdani/ObstacleAvoidance.fll");

 # Python
 import fuzzylite as fl

 engine = fl.FllImporter().from_file("examples/mamdani/ObstacleAvoidance.fll")

 // Java
 importcom.fuzzylite.*;

 Engineengine=newFllImporter().fromFile("examples/mamdani/ObstacleAvoidance.fll");

 #include"fl/Headers.h"

 usingnamespacefl;

 Engine*engine=newEngine;
 engine->setName("ObstacleAvoidance");

 InputVariable*obstacle=newInputVariable;
 obstacle->setName("obstacle");
 obstacle->setDescription("");
 obstacle->setEnabled(true);
 obstacle->setRange(0.000,1.000);
 obstacle->setLockValueInRange(false);
 obstacle->addTerm(newRamp("left",1.000,0.000));
 obstacle->addTerm(newRamp("right",0.000,1.000));
 engine->addInputVariable(obstacle);

 OutputVariable*mSteer=newOutputVariable;
 mSteer->setName("mSteer");
 mSteer->setDescription("");
 mSteer->setEnabled(true);
 mSteer->setRange(0.000,1.000);
 mSteer->setLockValueInRange(false);
 mSteer->setAggregation(newMaximum);
 mSteer->setDefuzzifier(newCentroid(100));
 mSteer->setDefaultValue(fl::nan);
 mSteer->setLockPreviousValue(false);
 mSteer->addTerm(newRamp("left",1.000,0.000));
 mSteer->addTerm(newRamp("right",0.000,1.000));
 engine->addOutputVariable(mSteer);

 RuleBlock*mamdani=newRuleBlock;
 mamdani->setName("mamdani");
 mamdani->setDescription("");
 mamdani->setEnabled(true);
 mamdani->setConjunction(fl::null);
 mamdani->setDisjunction(fl::null);
 mamdani->setImplication(newAlgebraicProduct);
 mamdani->setActivation(newGeneral);
 mamdani->addRule(Rule::parse("if obstacle is left then mSteer is right",engine));
 mamdani->addRule(Rule::parse("if obstacle is right then mSteer is left",engine));
 engine->addRuleBlock(mamdani);

 import fuzzylite as fl

 engine = fl.Engine(
 name="ObstacleAvoidance",
 input_variables=[
 fl.InputVariable(
 name="obstacle",
 minimum=0.0,
 maximum=1.0,
 lock_range=False,
 terms=[fl.Ramp("left", 1.0, 0.0), fl.Ramp("right", 0.0, 1.0)],
 )
 ],
 output_variables=[
 fl.OutputVariable(
 name="mSteer",
 minimum=0.0,
 maximum=1.0,
 lock_range=False,
 lock_previous=False,
 default_value=fl.nan,
 aggregation=fl.Maximum(),
 defuzzifier=fl.Centroid(resolution=100),
 terms=[fl.Ramp("left", 1.0, 0.0), fl.Ramp("right", 0.0, 1.0)],
 )
 ],
 rule_blocks=[
 fl.RuleBlock(
 name="mamdani",
 conjunction=None,
 disjunction=None,
 implication=fl.AlgebraicProduct(),
 activation=fl.General(),
 rules=[
 fl.Rule.create("if obstacle is left then mSteer is right"),
 fl.Rule.create("if obstacle is right then mSteer is left"),
 ],
 )
 ],
 )

 importcom.fuzzylite.*;

 Engineengine=newEngine();
 engine.setName("ObstacleAvoidance");

 InputVariableobstacle=newInputVariable();
 obstacle.setName("obstacle");
 obstacle.setDescription("");
 obstacle.setEnabled(true);
 obstacle.setRange(0.000,1.000);
 obstacle.setLockValueInRange(false);
 obstacle.addTerm(newRamp("left",1.000,0.000));
 obstacle.addTerm(newRamp("right",0.000,1.000));
 engine.addInputVariable(obstacle);

 OutputVariablemSteer=newOutputVariable();
 mSteer.setName("mSteer");
 mSteer.setDescription("");
 mSteer.setEnabled(true);
 mSteer.setRange(0.000,1.000);
 mSteer.setLockValueInRange(false);
 mSteer.setAggregation(newMaximum());
 mSteer.setDefuzzifier(newCentroid(100));
 mSteer.setDefaultValue(Double.NaN);
 mSteer.setLockPreviousValue(false);
 mSteer.addTerm(newRamp("left",1.000,0.000));
 mSteer.addTerm(newRamp("right",0.000,1.000));
 engine.addOutputVariable(mSteer);

 RuleBlockmamdani=newRuleBlock();
 mamdani.setName("mamdani");
 mamdani.setDescription("");
 mamdani.setEnabled(true);
 mamdani.setConjunction(null);
 mamdani.setDisjunction(null);
 mamdani.setImplication(newAlgebraicProduct());
 mamdani.setActivation(newGeneral());
 mamdani.addRule(Rule.parse("if obstacle is left then mSteer is right",engine));
 mamdani.addRule(Rule.parse("if obstacle is right then mSteer is left",engine));
 engine.addRuleBlock(mamdani);

Features¶

`6` Controller Types¶

Mamdani, Takagi-Sugeno, Larsen, Tsukamoto, Inverse Tsukamoto, Hybrid.

`25` Linguistic Terms¶

5 Basic: Triangle, Trapezoid, Rectangle, Discrete, SemiEllipse.

8 Extended: Bell, Cosine, Gaussian, GaussianProduct, PiShape, SigmoidDifference, SigmoidProduct, Spike.

7 Edges: Arc, Binary, Concave, Ramp, Sigmoid, SShape, ZShape.

3 Functions: Constant, Linear, Function.

2 Special: Aggregated, Activated.

`7` Activation methods¶

General, Proportional, Threshold, First, Last, Lowest, Highest.

`9` T-Norms for Conjunction and Implication¶

Minimum, AlgebraicProduct, BoundedDifference, DrasticProduct, EinsteinProduct, HamacherProduct, NilpotentMinimum, LambdaNorm, FunctionNorm.

`11` S-Norms for Disjunction and Aggregation¶

Maximum, AlgebraicSum, BoundedSum, DrasticSum, EinsteinSum, HamacherSum, NilpotentMaximum, NormalizedSum, UnboundedSum, LambdaNorm, FunctionNorm.

`7` Defuzzifiers¶

5 Integral: Centroid, Bisector, SmallestOfMaximum, LargestOfMaximum, MeanOfMaximum.

2 Weighted: WeightedAverage, WeightedSum.

`7` Hedges¶

Any, Not, Extremely, Seldom, Somewhat, Very, Function.

`3` Importers¶

FuzzyLite Language (fll), Fuzzy Inference System (fis), Fuzzy Control Language (fcl).

`8` Exporters¶

Python, C++, Java, FuzzyLite Language (fll), FuzzyLite Dataset (fld), R script, Fuzzy Inference System (fis), Fuzzy Control Language (fcl).

`30+` Examples¶

Mamdani, Takagi-Sugeno, Tsukamoto, and Hybrid controllers from fuzzylite, Octave, and Matlab.

Examples in C++, Python, Java, fll, fld, R, fis, and fcl.

Flexibility¶

Easily extend the library by adding your own linguistic terms, activation methods, T-Norms and S-Norms, defuzzifiers, and hedges.

Reference¶

FuzzyLite-Libraries.pdf
PDF Article

If you are using the FuzzyLite Libraries, please cite our article as follows:

Juan Rada-Vilela. The FuzzyLite Libraries for Fuzzy Logic Control, 2018. URL https://fuzzylite.com.

Or using bibtex:

 @misc{
 fl::fuzzylite,
 author={Juan Rada-Vilela},
 title={The FuzzyLite Libraries for Fuzzy Logic Control},
 url={https://fuzzylite.com},
 year={2018}
 }

fuzzylite® is a registered trademark of FuzzyLite
pyfuzzylite™ is a trademark of FuzzyLite
jfuzzylite™ is a trademark of FuzzyLite
QtFuzzyLite™ is a trademark of FuzzyLite

The FuzzyLite Libraries for Fuzzy Logic Control¶

License¶

Repositories¶

fuzzylite (C++) ¶

pyfuzzylite (Python) ¶

jfuzzylite (Java) ¶

help ¶

Examples¶

Features¶

6 Controller Types¶

25 Linguistic Terms¶

7 Activation methods¶

9 T-Norms for Conjunction and Implication¶

11 S-Norms for Disjunction and Aggregation¶

7 Defuzzifiers¶

7 Hedges¶

3 Importers¶

8 Exporters¶

30+ Examples¶