TY - JOUR
T1 - Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems
AU - Kronberger, Gabriel
AU - Kommenda, Michael
AU - Lughofer, Edwin
AU - Saminger-Platz, Susanne
AU - Promberger, Andreas
AU - Nickel, Falk
AU - Winkler, Stephan
AU - Affenzeller, Michael
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
KW - Tribological systems
KW - Robust fuzzy modeling
KW - Generalized Takagi-Sugeno fuzzy systems
KW - Symbolic regression
KW - Multi-objective genetic programming
KW - Tribological systems
KW - Robust fuzzy modeling
KW - Generalized Takagi-Sugeno fuzzy systems
KW - Symbolic regression
KW - Multi-objective genetic programming
UR - http://www.scopus.com/inward/record.url?scp=85047006624&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2018.04.048
DO - 10.1016/j.asoc.2018.04.048
M3 - Article
VL - 69
SP - 610
EP - 624
JO - Applied Soft Computing
JF - Applied Soft Computing
ER -