Learning in dynamic environments: application to the diagnosis of evolving systems
Moamar Sayed-Mouchaweh, Omar Ayad, Noureddine Malki
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.40How to use a DOI?
- Classification; incremental learning; dynamic environments; evolving systems.
- In dynamic environments, data characteristics may drift over time. This leads to deteriorate dramatically the performance of incremental learning algorithms over time. This is because of the use of data which is no more consistent with the characteristics of new incoming one. In this paper, an approach for learning in dynamic environments is proposed. This approach integrates a mechanism to use only the recent and useful patterns to update the classifier without a "catastrophic forgetting". This approach is used for the acoustic leak detection in the steam generator unit of the nuclear power generator "Prototype Fast React".
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Moamar Sayed-Mouchaweh AU - Omar Ayad AU - Noureddine Malki PY - 2011/08 DA - 2011/08 TI - Learning in dynamic environments: application to the diagnosis of evolving systems BT - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11) PB - Atlantis Press SP - 396 EP - 401 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2011.40 DO - https://doi.org/10.2991/eusflat.2011.40 ID - Sayed-Mouchaweh2011/08 ER -