Robust fuzzy modeling and symbolic regression for establishing accurate and interpretable prediction models in supervising tribological systems

Edwin Lughofer, Gabriel Kronberger, Michael Kommenda, Susanne Saminger-Platz, Andreas Promberger, Falk Nickel, Stephan Winkler, Michael Affenzeller

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

5 Citations (Scopus)

Abstract

In this contribution, we discuss data-based methods for building regression models for predicting important characteristics of tribological systems (such as the friction coefficient), with the overall goal of improving and partially automatizing the design and dimensioning of tribological systems. In particular, we focus on two methods for synthesis of interpretable and potentially non-linear regression models: (i) robust fuzzy modeling and (ii) enhanced symbolic regression using genetic programming, both embedding new methodological extensions. The robust fuzzy modeling technique employs generalized Takagi-Sugeno fuzzy systems. Its learning engine is based on the Gen-Smart-EFS approach, which in this paper is (i) adopted to the batch learning case and (ii) equipped with a new enhanced regularized learning scheme for the rule consequent parameters. Our enhanced symbolic regression method addresses (i) direct gradient-based optimization of numeric constants (in a kind of memetic approach) and (ii) multi-objectivity by adding complexity as a second optimization criterion to avoid over-fitting and to increase transparency of the resulting models. The comparison of the new extensions with state-of-the-art non-linear modeling techniques based on nine different learning problems (including targets wear, friction coefficients, temperatures and NVH) shows indeed similar errors on separate validation data, but while (i) achieving much less complex models and (ii) allowing some insights into model structures and components, such that they could be confirmed as very reliable by the experts working with the concrete tribological system.

Original languageEnglish
Title of host publicationFCTA 2016 - 8th International Conference on Fuzzy Computation Theory and Applications
EditorsJuan Julian Merelo, Fernando Melicio, Jose M. Cadenas, Antonio Dourado, Kurosh Madani, Antonio Ruano, Joaquim Filipe
PublisherSciTePress
Pages51-63
Number of pages13
ISBN (Electronic)9789897582011
ISBN (Print)978-989-758-201-1
DOIs
Publication statusPublished - 2016
Event8th International Joint Conference on Computational Intelligence, IJCCI 2016 - Porto, Portugal
Duration: 9 Nov 201611 Nov 2016

Publication series

NameIJCCI 2016 - Proceedings of the 8th International Joint Conference on Computational Intelligence
Volume2

Conference

Conference8th International Joint Conference on Computational Intelligence, IJCCI 2016
CountryPortugal
CityPorto
Period09.11.201611.11.2016

Keywords

  • Enhanced Regularized Learning
  • Generalized Takagi-Sugeno Fuzzy Systems
  • Multi-objective Accuracy/Complexity Tradeoff
  • Robust Fuzzy Modeling
  • Symbolic Regression
  • Tribological Systems

Fingerprint Dive into the research topics of 'Robust fuzzy modeling and symbolic regression for establishing accurate and interpretable prediction models in supervising tribological systems'. Together they form a unique fingerprint.

Cite this