title: |
Learning in dynamic environments: application to the diagnosis of evolving systems |
|
publication: |
||
part of series: |
Advances in Intelligent Systems Research | |
| pages: | 396 - 401 | |
DOI: |
To be assigned soon (how to use a DOI) | |
author(s): |
Moamar Sayed-Mouchaweh, Omar Ayad and Noureddine Malki |
|
publication date: |
July 2011 |
|
keywords: |
Classification; incremental learning;
dynamic environments; evolving systems. |
|
abstract: |
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". |
|
copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
|
full text: |