Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems

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@Article{kronberger2018using,

• 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",
• title = "Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems",
• journal = "Applied Soft Computing",
• year = "2018",
• volume = "69",
• pages = "610--624",
• month = aug,
• keywords = "genetic algorithms, genetic programming, Tribological systems, Robust fuzzy modelling, Generalized Takagi-Sugeno fuzzy systems, Symbolic regression, Multi-objective genetic programming",
• publisher = "Elsevier",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494618302394?via%3Dihub",
• DOI = "doi:10.1016/j.asoc.2018.04.048",
• 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