title: Common Optimization of Adaptive Preprocessing Units and a Neural Network during the Learning Period. Application in EEG Pattern Recognition.
abstract: In this study, a proposition of simultaneous training of the neural network (multilayer perceptron) and adaptive preprocessing unit is presented. This cooperation enables the network to affect the preprocessing and as a consequence to vary the locations of pattern vectors in a feature space. Thus, during the learning process the network tries to find a good separation of classes of patterns, which results in convergence of the whole learning process. The strategy was developed in order to make efficient EEG monitoring in neonates possible. A comparison of the method presented herein with the known learning strategies for neural networks shows the need for using it as an alternative learning process. The convergence of the whole system is also discussed. Copyright 1997 Elsevier Science Ltd.
authors.affiliation: Department of Biomedical Engineering and Computer Sciences, Technical University Ilmenau, Germany
authors.forename: Gert
authors.initials: G
authors.lastname: Griessbach
authors.forename: Michael
authors.initials: M
authors.lastname: Eiselt
authors.forename: Jens
authors.initials: J
authors.lastname: Dörschel
authors.forename: Herbert
authors.initials: H
authors.lastname: Witte
authors.forename: Miroslaw
authors.initials: M
authors.lastname: Galicki