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.
pyfuzzylite 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.
pip install pyfuzzylite
Documentation: fuzzylite.github.io/pyfuzzylite/
(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. With fuzzylite: Fuzzy Inference System fis, Fuzzy Control
Language fcl.
(7) Exporters: Python, FuzzyLite Language fll, FuzzyLite Dataset fld. With fuzzylite: 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: py, fll, fld. With fuzzylite: C++, Java, R, fis, and fcl.
# 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# Python
import fuzzylite as fl
engine = fl.FllImporter().from_file("examples/mamdani/ObstacleAvoidance.fll")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"),
],
)
],
)# single `float` operation
engine.input_variable("obstacle").value = 0.5
engine.process()
print("y =", engine.output_variable("mSteer").value)
# > y = 0.5
print("ỹ =", engine.output_variable("mSteer").fuzzy_value())
# > ỹ = 0.500/left + 0.500/right
# vectorization
engine.input_variable("obstacle").value = fl.array([0, 0.25, 0.5, 0.75, 1.0])
engine.process()
print("y =", repr(engine.output_variable("mSteer").value))
# > y = array([0.6666665 , 0.62179477, 0.5 , 0.37820523, 0.3333335 ])
print("ỹ =", repr(engine.output_variable("mSteer").fuzzy_value()))
# > ỹ = array(['0.000/left + 1.000/right',
# '0.250/left + 0.750/right',
# '0.500/left + 0.500/right',
# '0.750/left + 0.250/right',
# '1.000/left + 0.000/right'], dtype='<U26')Please refer to the documentation for more information: fuzzylite.github.io/pyfuzzylite/
All 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
jfuzzylite™, pyfuzzylite™ and QtFuzzyLite™ are trademarks of FuzzyLite