The FuzzyLite Libraries for Fuzzy Logic Control refer to fuzzylite
(C++), pyfuzzylite (Python),
and jfuzzylite (Java).
The goal of the FuzzyLite Libraries is to easily design and efficiently operate fuzzy logic controllers following an object-oriented programming model with minimal dependency on external libraries.
fuzzylite is dual-licensed under the GNU GPL 3.0 and under a
proprietary license for commercial purposes.
You are strongly encouraged to support the development of the FuzzyLite Libraries by purchasing a license
of QtFuzzyLite.
QtFuzzyLite is the best graphical user interface available to easily design and
directly operate fuzzy logic controllers in real time. Available for Windows, Mac, and Linux, its goal is to
significantly speed up the design of your fuzzy logic controllers, while providing a very useful, functional
and beautiful user interface.
Please, download it and check it out for free at fuzzylite.com/downloads.
Visit fuzzylite.com/documentation
(6) Controllers: 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) Conjunction and Implication (T-Norms): Minimum, AlgebraicProduct, BoundedDifference, DrasticProduct, EinsteinProduct, HamacherProduct, NilpotentMinimum, LambdaNorm, FunctionNorm.
(11) Disjunction and Aggregation (S-Norms): 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.
(7) Exporters: C++, Java, FuzzyLite Language fll, FuzzyLite Dataset fld, R script, Fuzzy Inference
System fis, Fuzzy Control Language fcl.
(30+) Examples of Mamdani, Takagi-Sugeno, Tsukamoto, and Hybrid controllers from fuzzylite, Octave, and Matlab,
each included in the following formats: C++, Java, fll, fld, R, fis, and fcl.
#File: 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//File: ObstacleAvoidance.cpp
#include <fl/Headers.h>
fl::Engine* engine = fl::FllImporter().fromFile("ObstacleAvoidance.fll");//File: ObstacleAvoidance.cpp
#include <fl/Headers.h>
using namespace fuzzylite;
Engine* engine = new Engine;
engine->setName("ObstacleAvoidance");
engine->setDescription("");
InputVariable* obstacle = new InputVariable;
obstacle->setName("obstacle");
obstacle->setDescription("");
obstacle->setEnabled(true);
obstacle->setRange(0.000, 1.000);
obstacle->setLockValueInRange(false);
obstacle->addTerm(new Ramp("left", 1.000, 0.000));
obstacle->addTerm(new Ramp("right", 0.000, 1.000));
engine->addInputVariable(obstacle);
OutputVariable* mSteer = new OutputVariable;
mSteer->setName("mSteer");
mSteer->setDescription("");
mSteer->setEnabled(true);
mSteer->setRange(0.000, 1.000);
mSteer->setLockValueInRange(false);
mSteer->setAggregation(new Maximum);
mSteer->setDefuzzifier(new Centroid(100));
mSteer->setDefaultValue(fl::nan);
mSteer->setLockPreviousValue(false);
mSteer->addTerm(new Ramp("left", 1.000, 0.000));
mSteer->addTerm(new Ramp("right", 0.000, 1.000));
engine->addOutputVariable(mSteer);
RuleBlock* mamdani = new RuleBlock;
mamdani->setName("mamdani");
mamdani->setDescription("");
mamdani->setEnabled(true);
mamdani->setConjunction(fl::null);
mamdani->setDisjunction(fl::null);
mamdani->setImplication(new AlgebraicProduct);
mamdani->setActivation(new General);
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);using namespace fuzzylite;
std::string status;
if (not engine->isReady(&status))
throw Exception("[engine error] engine is not ready:\n" + status, FL_AT);
InputVariable* obstacle = engine->getInputVariable("obstacle");
OutputVariable* steer = engine->getOutputVariable("steer");
for (int i = 0; i <= 50; ++i){
scalar location = obstacle->getMinimum() + i * (obstacle->range() / 50);
obstacle->setValue(location);
engine->process();
FL_LOG("obstacle.input = " << Op::str(location) <<
" => " << "steer.output = " << Op::str(steer->getValue()));
}Once you have an engine written in C++, you can compile it to create an executable file which links to the fuzzylite
library. The linking can be either static or dynamic. Basically, the differences between static and dynamic linking are
the following.
Static linking includes the fuzzylite library into your executable file, hence increasing its size, but the
executable no longer needs to have access to the fuzzylite library files.
Dynamic linking does not include the fuzzylite library into your executable file, hence reducing its size, but the
executable needs to have access to the fuzzylite shared library file. When using dynamic linking, make sure that the
shared library files are either in the same directory as the executable, or are reachable via environmental variables:
rem Windows:
set PATH="\path\to\fuzzylite\release\bin;%PATH%"#Unix:
export LD_LIBRARY_PATH="/path/to/fuzzylite/release/bin/:$LD_LIBRARY_PATH"The commands to compile your engine in Windows are the following:
C++11 (default)
rem static linking:
cl.exe ObstacleAvoidance.cpp fuzzylite-static.lib /Ipath/to/fuzzylite /EHsc /MDrem dynamic linking:
cl.exe ObstacleAvoidance.cpp fuzzylite.lib /Ipath/to/fuzzylite /DFL_IMPORT_LIBRARY /EHsc /MD C++98
rem static linking:
cl.exe ObstacleAvoidance.cpp fuzzylite-static.lib /Ipath/to/fuzzylite /DFL_CPP98=ON /EHsc /MDrem dynamic linking:
cl.exe ObstacleAvoidance.cpp fuzzylite.lib /Ipath/to/fuzzylite /DFL_IMPORT_LIBRARY /DFL_CPP98=ON /EHsc /MD The commands to compile your engine in Unix are the following:
C++11 (default)
#static linking
g++ ObstacleAvoidance.cpp -o ObstacleAvoidance -I/path/to/fuzzylite -L/path/to/fuzzylite/release/bin -lfuzzylite-static --std=c++11#dynamic linking
g++ ObstacleAvoidance.cpp -o ObstacleAvoidance -I/path/to/fuzzylite -L/path/to/fuzzylite/release/bin -lfuzzyliteC++98
#static linking
g++ ObstacleAvoidance.cpp -o ObstacleAvoidance -I/path/to/fuzzylite -L/path/to/fuzzylite/release/bin -lfuzzylite-static -DFL_CPP98=ON#dynamic linking
g++ ObstacleAvoidance.cpp -o ObstacleAvoidance -I/path/to/fuzzylite -L/path/to/fuzzylite/release/bin -lfuzzylite -DFL_CPP98=ONAlternatively, you can use CMake to build your project linking to fuzzylite. Please, refer to the example application
available at examples/application.
You can build the fuzzylite library from source using CMake (cmake.org).
Check .github/workflows for details.
cmake -B build/ -G"Unix Makefiles" .
cmake --build build/ --parallel
ctest --test-dir build/cmake -B build/ -G"NMake Makefiles" .
cmake --build build/
ctest --test-dir build/The following building options available:
-DFL_USE_FLOAT=ON builds the binaries using the fl::scalar data type as a float instead of double. By default,
the binaries are built using -DFL_USE_FLOAT=OFF. If fuzzylite is built with -DFL_USE_FLOAT=ON, then the
applications linking to fuzzylite also need to specify this compilation flag.
-DFL_CPP98=ON builds binaries using C++98 features instead of C++11. By default, the binaries are built
using -DFL_CPP98=OFF. If you use C++98, you will not be able to benchmark the performance of your engine using
the Benchmark class, and you will not be able to run any of the tests.
-DFL_BACKTRACE=OFF disables the backtrace information in case of errors. By default, the binaries are built
using -DFL_BACKTRACE=ON. In Windows, the backtrace information requires the external library dbghelp, which is
generally available in your system.
The source code of fuzzylite is very well documented using doxygen formatting, and the
documentation is available at fuzzylite.com/documentation. If you want to
generate the documentation locally, you can produce the html documentation from the file Doxyfile using
the command line: doxygen Doxyfile. The documentation will be created in the docs folder.
After building from source, the following are the relevant binaries that will be created in Release mode. In Debug
mode, the file names end with -debug (e.g., fuzzylite-debug.exe).
- console application:
fuzzylite.exe - shared library:
fuzzylite.dll,fuzzylite.lib - static library:
fuzzylite-static.lib
- console application:
fuzzylite - shared library:
libfuzzylite.so - static library:
libfuzzylite-static.a
- console application:
fuzzylite - shared library:
libfuzzylite.dylib - static library:
libfuzzylite-static.a
The console application of fuzzylite allows you to import and export your engines. Its usage can be obtained executing
the console binary. In addition, the console can be set in interactive mode. The FuzzyLite Interactive Console allows
you to evaluate a given controller by manually providing the input values. The interactive console is triggered by
specifying an input file and an output format. For example, to interact with the ObstacleAvoidance controller, the
interactive console is launched as follows:
fuzzylite -i ObstacleAvoidance.fll -of fldAll contributions are welcome, provided they follow the following guidelines:
- Source code is consistent with standards in the library
- Contribution is properly documented and tested, raising issues where appropriate
- Contribution is licensed under the FuzzyLite License
If you are using the FuzzyLite Libraries, please cite the following reference in your article:
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 Limited
jfuzzylite™ is a trademark of FuzzyLite Limited
pyfuzzylite™ is a trademark of FuzzyLite Limited
QtFuzzyLite™ is a trademark of FuzzyLite Limited