Using Robust Generalized Fuzzy Modeling and Enhanced Symbolic Regression to Model Tribological Systems
Sprache des Titels:
Englisch
Original Kurzfassung:
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 modeling 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 modeling 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 modeling 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.
Forschungs-