Moamar Sayed Mouchaweh

Learn More
We use learning methods to build classi5ers in using a set of training samples. A classi5er is capable to assign a new sample into one of the di6erent learnt classes. In a non-stationary work environment, a classi5er must be retrained every time a new sample is classi5ed, to obtain new knowledge from it. This is an impractical solution since it requires the(More)
This article presents a plastic injection moulding monitoring module based on knowledge built on-line using feedback from production data. A fuzzy classifier was especially developed for this application. It is based on unsupervised and supervised classification methods. The role of the first one is to identify the functioning modes of the process whereas(More)
In this paper, we propose to use the evidence classification method Fuzzy Pattern Matching (FPM) to realize the diagnosis in real time. Then we show how the integration of the incremental learning in FPM allows to complete the missing knowledge in the database and to follow, in real time, the changes on the shape of classes. Finally we develop the(More)