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{
"category": "SciPy 2013",
"language": "English",
"slug": "scikit-fuzzy-a-new-scipy-toolkit-for-fuzzy-logic",
"speakers": [],
"tags": [
"Tech"
],
"id": 2019,
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"title": "Scikit-Fuzzy: A New SciPy Toolkit for Fuzzy Logic; SciPy 2013 Presentation",
"summary": "Authors: Warner, Joshua, Mayo Clinic Department of Biomedical Engineering; Ottesen, Hal H., Adjunct Professor\n\nTrack: General\n\nScikit-fuzzy is a robust set of foundational tools for problems involving fuzzy logic and fuzzy systems. This area has been a challenge for the scientific Python community, largely because the common first exposure to this topic is through the MATLAB\u00ae Fuzzy Logic Toolbox\u2122. This talk officially introduces a general set of original fuzzy logic algorithms to the scientific Python community which predate the commercial toolbox, were released under the 3-clause BSD license, and were translated to Python by an author who never used the MathWorks\u00c2\u00ae Fuzzy Logic Toolbox\u2122.\n\nThe current capabilities of scikit-fuzzy include: fuzzy membership function generation; fuzzy set operations; lambda-cuts; fuzzy mathematics including Zadeh's extension principle, the vertex method, and the DSW method; fuzzy implication given an IF THEN system of fuzzy rules (via Mamdani [min] or Larsen [product] implication); various defuzzification algorithms; fuzzy c-means clustering; and Fuzzy Inference Ruled by Else-action (FIRE) denoising of 1d or 2d signals.\n\nThe goals of scikit-fuzzy are to provide the community with a robust toolkit of independently developed and implemented fuzzy logic algorithms, filling a void in the capabilities of scientific and numerical Python, and to increase the attractiveness of scientific Python as a valid alternative to closed-source options. Scikit-fuzzy is structured similarly to scikit-learn and scikit-image, current source code is available on GitHub, and pull requests are welcome.",
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