fuzzylite® 6.0

A Fuzzy Logic Control Library in C++

By: Juan Rada-Vilela, Ph.D.

Released: 20/March/2017

License: GPL v3 Build Status Build status Coverity Status


Table of Contents

License
Introduction
Features
Example
Compile, Link, and Execute
Bulding from Source
Binaries
What’s new
What’s next


License

fuzzylite 6.0 is licensed under the GNU General Public License (GPL) 3.0. You are strongly encouraged to support the development of the FuzzyLite Libraries by purchasing a license of QtFuzzyLite 6.

QtFuzzyLite 6 is the new and (very likely) 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 www.fuzzylite.com/downloads/.


Introduction

fuzzylite is a free and open-source fuzzy logic control library programmed in C++ for multiple platforms (e.g., Windows, Linux, Mac, iOS). jfuzzylite is the equivalent library for Java and Android platforms. Together, they are the FuzzyLite Libraries for Fuzzy Logic Control.

The goal of the FuzzyLite Libraries is to easily design and efficiently operate fuzzy logic controllers following an object-oriented programming model without relying on external libraries.

Reference

If you are using the FuzzyLite Libraries, please cite the following reference in your article:

Juan Rada-Vilela. fuzzylite: a fuzzy logic control library, 2017. URL http://www.fuzzylite.com/.

 @misc{fl::fuzzylite,
 author={Juan Rada-Vilela},
 title={fuzzylite: a fuzzy logic control library},
 url={http://www.fuzzylite.com/},
 year={2017}}

Documentation

The documentation for the fuzzylite library is available at: www.fuzzylite.com/documentation/.


Features

(6) Controllers: Mamdani, Takagi-Sugeno, Larsen, Tsukamoto, Inverse Tsukamoto, Hybrids

(21) Linguistic terms: (4) Basic: triangle, trapezoid, rectangle, discrete.
(9) Extended: bell, cosine, gaussian, gaussian product, pi-shape, sigmoid difference, sigmoid product, spike.
(5) Edges: binary, concave, ramp, sigmoid, s-shape, z-shape.
(3) Functions: constant, linear, function.

(7) Activation methods: general, proportional, threshold, first, last, lowest, highest.

(8) Conjunction and Implication (T-Norms): minimum, algebraic product, bounded difference, drastic product, einstein product, hamacher product, nilpotent minimum, function.

(10) Disjunction and Aggregation (S-Norms): maximum, algebraic sum, bounded sum, drastic sum, einstein sum, hamacher sum, nilpotent maximum, normalized sum, unbounded sum, function.

(7) Defuzzifiers: (5) Integral: centroid, bisector, smallest of maximum, largest of maximum, mean of maximum.
(2) Weighted: weighted average, weighted sum.

(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.


Example

FuzzyLite Language

#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"

int main(int argc, char* argv[]){
    using namespace fl;
    Engine* engine = FllImporter().fromFile("ObstacleAvoidance.fll");

    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("mSteer");

    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()));
    }
}

C++

//File: ObstacleAvoidance.cpp
#include "fl/Headers.h"

int main(int argc, char* argv[]){
    using namespace fl;
    //Code automatically generated with fuzzylite 6.0.

    using namespace fl;

    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);

    std::string status;
    if (not engine->isReady(&status))
        throw Exception("[engine error] engine is not ready:n" + status, FL_AT);

    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()));
    }
}

Compile, Link, and Execute

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="pathtofuzzylitereleasebin;%PATH%"
#Unix:
export LD_LIBRARY_PATH="/path/to/fuzzylite/release/bin/:$LD_LIBRARY_PATH"

Windows

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 /MD
rem 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 /MD
rem dynamic linking:
cl.exe ObstacleAvoidance.cpp fuzzylite.lib /Ipath/to/fuzzylite /DFL_IMPORT_LIBRARY /DFL_CPP98=ON /EHsc /MD 

Unix

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 -lfuzzylite -Wno-non-literal-null-conversion

C++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=ON -Wno-non-literal-null-conversion

CMake

Alternatively, you can use CMake to build your project linking to fuzzylite. Please, refer to the example application available at examples/application.


Building from Source

You can build the fuzzylite library from source using CMake (cmake.org).

The files fuzzylite/build.bat and fuzzylite/build.sh are build scripts for the Windows and Unix platforms, respectively.
After building from source, the resulting binaries will be located in the sub-folders fuzzylite/release/bin and fuzzylite/debug/bin. The usage of these scripts is presented as follows.

Windows

> build.bat help
Usage:  build.bat [options]
where   [options] can be any of the following:
    all          builds fuzzylite in debug and release mode (default)
    debug        builds fuzzylite in debug mode
    release      builds fuzzylite in release mode
    clean        erases previous builds
    help         shows this information

Unix

$ ./build.sh help
Usage:  [bash] ./build.sh [options]
where   [options] can be any of the following:
    all          builds fuzzylite in debug and release mode (default)
    debug        builds fuzzylite in debug mode
    release      builds fuzzylite in release mode
    clean        erases previous builds
    help         shows this information

Building Options

For advanced building options, please check the contents of fuzzylite/build.bat or fuzzylite/build.sh, and the contents of fuzzylite/CMakeLists.txt.

The following building options available:

  • -DFL_USE_FLOAT=ON builds the binaries utilizing the fl::scalar data type as a float represented in 4 bytes. By default, the binaries are built utilizing -DFL_USE_FLOAT=OFF to utilize fl::scalar as a double represented in 8 bytes and hence providing better accuracy. 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 utilizing C++98 features. By default, fuzzylite is built with -DFL_CPP98=OFF to utilize C++11 features. If compiling for C++98, be aware that you will not be able to benchmark the performance of your engine using the Benchmark class.

  • -DFL_BACKTRACE=OFF disables the backtrace information in case of errors (default is ON). In Windows, the backtrace information requires the external library dbghelp, which is generally available in your system.

  • -DCMAKE_BUILD_TYPE=[Debug|Release] sets the mode of your build. You can only build one mode at a time with a single CMake script.

Documentation

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 documentation folder.


Binaries

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).

Windows

  • console application: fuzzylite.exe
  • shared library: fuzzylite.dll, fuzzylite.lib
  • static library: fuzzylite-static.lib

Linux

  • console application: fuzzylite
  • shared library: libfuzzylite.so
  • static library: libfuzzylite-static.a

Mac

  • console application: fuzzylite
  • shared library: libfuzzylite.dylib
  • static library: libfuzzylite-static.a

Console

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 fld

What’s New?

  • The FuzzyLite Libraries, namely fuzzylite and jfuzzylite, both in version 6.0, are licensed under the GNU General Public License version 3 and a private commercial license.

  • By default, fuzzylite builds using C++11 instead of C++98.

  • Important performance improvements.

  • Refactored the following names for the operation of engines: from activation operator to implication operator, from accumulation operator to aggregation operator.

  • Renamed the term Accumulated to Aggregated.

  • New activation methods decouple the activation of rules from the rule block and provide different methods for activating rules (see Activation Methods).

  • New class ActivationFactory provides a factory of activation methods.

  • New class Benchmark to evaluate the performance and accuracy of engines.

  • New class Complexity to estimate the computational complexity of an engine.

  • New class RScriptExporter to export the surfaces of an engine using the ggplot2 library.

  • New class Binary term for binary edges.

  • New UnboundedSum S-Norm in SNormFactory.

  • New classes SNormFunction and TNormFunction to create custom functions on any two values using the Function class.

  • Added description strings to Engine, Variable and RuleBlock

  • Privatized previously protected members of classes and subclasses of Term, Variable, Rule, Defuzzifier, [Cloning|Construction]Factory, Importer, Exporter, amongst others.

  • Improved portability by replacing int for std::size_t where necessary, thereby additionally removing warnings in Windows 64bit

  • Deleted Operation.cpp and inlined its methods into Operation.h

  • Updated .travis.yml to use Docker, and build using g++ (versions 6, 5, 4.9, 4.8, 4.7) and clang (versions 3.8, 3.7, 3.6, and 3.5).

  • Added appveyor.yml to use continuous integration in Windows under Visual Studio 2013 and 2015.

  • Added some unit tests and support for future unit tests.

  • Bug fixes.

  • New example of hybrid engines.

  • New example on obstacle avoidance for Mamdani, Takagi-Sugeno, and Hybrid engines.

  • New R scripts for each example and its respective surfaces in pdf formats.

Bug fixes

  • Fixed bug in CloningFactory::deregisterObject(). Bug: Object was deleted before removing it from the map, leaving an invalid object in the map which would cause a segmentation fault. Solution: Remove the object from the map before deleting it.
  • Fixed bug causing segmentation fault when malformed term in FuzzyLite Language
  • Fixed bug computing the NormalizedSum S-Norm.
  • Fixed bug in RuleBlock to reset and clone the implication operator. Bug: implication operator is not copied and reset. Fix: copy and reset implication operator when cloning the RuleBlock.
  • Fixed bug in Function term. Bug: given a formula = “tan(y)” and a map[“y”] = 1.0, and executing Function::load(formula), then the map of variables is reset because load() calls unload() first, causing the deregistration of variable y. Solution: Removed method unload() from load(), thereby causing future load() not to reset variables.
  • Fixed bug in Function when enclosing variable in double parenthesis.

What’s Next?

  • Optimization of Fuzzy Logic Controllers
  • Type-2 Fuzzy Logic Controllers
  • Adaptive Neuro-Fuzzy Inference System (ANFIS)
  • Fuzzy C-means data clustering

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