Robustification of Self-Optimising Systems via Explicit Treatment of Uncertain Information

DOI

10.2991/ifsa-eusflat-15.2015.25How to use a DOI?

Keywords

Self-Optimising Systems, Uncertain Information, Uncertainty Treatment, Regression

Abstract

Uncertainty treatment in self-optimising systems touches two design-issues. Firstly, a valid estimation of uncertainties within the system is impossible beforehand as the uncertainties as well as the systems behaviour changes during run-time due to self-optimisation. Secondly, the design of a selfoptimising system needs to mediate between the often conflicting goals of optimality and robustness. Here we present the concept for a lightweight algorithmic add-on for self-optimising function approximators that enables to reflect uncertainties related to the current state and to flexibly combine optimality and robustness in one design. Illustrating examples of TS-fuzzy systems highlight the properties of our approach.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jan H. Schoenke
AU  - Werner Brockmann
PY  - 2015/06
DA  - 2015/06
TI  - Robustification of Self-Optimising Systems via Explicit Treatment of Uncertain Information
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 152
EP  - 161
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.25
DO  - 10.2991/ifsa-eusflat-15.2015.25
ID  - Schoenke2015/06
ER  -