Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems
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- @Article{kronberger2018using,
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author = "Gabriel Kronberger and Michael Kommenda and
Edwin Lughofer and Susanne Saminger-Platz and
Andreas Promberger and Falk Nickel and Stephan Winkler and
Michael Affenzeller",
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title = "Using robust generalized fuzzy modeling and enhanced
symbolic regression to model tribological systems",
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journal = "Applied Soft Computing",
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year = "2018",
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volume = "69",
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pages = "610--624",
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month = aug,
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keywords = "genetic algorithms, genetic programming, Tribological
systems, Robust fuzzy modelling, Generalized
Takagi-Sugeno fuzzy systems, Symbolic regression,
Multi-objective genetic programming",
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publisher = "Elsevier",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494618302394?via%3Dihub",
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DOI = "doi:10.1016/j.asoc.2018.04.048",
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abstract = "Tribological systems are mechanical systems that rely
on friction to transmit forces. The design and
dimensioning of such systems requires prediction of
various characteristic, such as the coefficient of
friction. The core contribution of this paper is the
analysis of two data-based modelling techniques which
can be used to produce accurate and at the same time
interpretable models for friction systems. We focus on
two methods for building interpretable and potentially
non-linear regression models: (i) robust fuzzy
modelling with batch processing and an enhanced
regularized learning scheme, and (ii) enhanced symbolic
regression using genetic programming. We compare our
results of both methods with state-of-the-art methods
and found that linear models are insufficient for
predicting the coefficient of friction, temperature,
wear, and noise-vibration-harshness rating of the
tribological systems, while the proposed robust fuzzy
modelling and the enhanced symbolic regression
approaches, as well as the state-of-the-art regression
techniques, are able to generate satisfactory models.
However, robust fuzzy modeling and enhanced symbolic
regression lead to simpler models with fewer parameters
that can be interpreted by domain experts",
- }
Genetic Programming entries for
Gabriel Kronberger
Michael Kommenda
Edwin Lughofer
Susanne Saminger-Platz
Andreas Promberger
Falk Nickel
Stephan M Winkler
Michael Affenzeller
Citations