Abstract
The aim of this contribution is to discuss suitability of extreme learning machine (ELM) approach for modeling multisource friction for motion control purposes. The specific features of multisource friction in mechatronic systems are defined, the main aspects of friction modeling by a standard ELM are investigated and some modifications are proposed to make it more suitable for specific demands of the discussed task. This allows to formulate some general remarks concerning properties of ELM for function approximation.
Chapter PDF
Similar content being viewed by others
References
Huang, G., Huang, G.-B., Song, S., You, K.: Trends in extreme learning machines: A review. Neural Networks 61, 32–48 (2015)
Liu, X., Lin, S., Fang, J., Xu, Z.: Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I). IEEE Transactions on Neural Networks and Learning Systems 26, 7–20 (2015)
Lin, S., Liu, X., Fang, J., Xu, Z.: Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II). IEEE Transactions on Neural Networks and Learning Systems 26, 21–34 (2015)
Chen, Z.X., Zhu, H.Y., Wang, Y.G.: A modified extreme learning machine with sigmoidal activation functions. Neural Comput. & Applic. 22, 541–550 (2013)
Parviainen, E., Riihimäki, J.: Connection between Extreme Learning Machine and Neural Network Kernel. In: Knowledge Discovery, Knowledge Engineering and Knowledge Management, Springer Berlin Heidelberg, pp. 122–135. Springer, Heidelberg (2013)
Wojewoda, J., Stefanski, A., Wiercigroch, M., Kapitaniak, T.: Hysteretic effects of dry friction: modelling and experimental studies. Phil. Trans. R. Soc. A 366, 747–765 (2008)
Kabziński, J.: One-Dimensional Linear Local Prototypes for Effective Selection of Neuro-Fuzzy Sugeno Model Initial Structure. In: IFIP WG 12.5 International Conference "Artificial Intelligence Applications and Innovations. Larnaca, Cyprus Springer. IFIP Series Berlin, pp. 62 – 69 (2010)
Kabziński, J.: Fuzzy friction modeling for adaptive control of mechatronic systems. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 381, pp. 185–195. Springer, Heidelberg (2012)
Kabziński, J., Kacerka, J.: TSK Fuzzy Modeling with Nonlinear Consequences. In: Iliadis, L. et al (eds.) AIAI 2014. IFIP AICT, vol. 436, pp. 498–507. Springer, Heidelberg (2014)
Jang, J.R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–684 (1993)
Khalil, H.: Nonlinear Systems, Macmilan Publishing Co., New York (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Kabziński, J. (2015). Is Extreme Learning Machine Effective for Multisource Friction Modeling?. In: Chbeir, R., Manolopoulos, Y., Maglogiannis, I., Alhajj, R. (eds) Artificial Intelligence Applications and Innovations. AIAI 2015. IFIP Advances in Information and Communication Technology, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-319-23868-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-23868-5_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23867-8
Online ISBN: 978-3-319-23868-5
eBook Packages: Computer ScienceComputer Science (R0)