Common optimization of adaptive preprocessing units and a neural network during the learning period. Application in EEG pattern recognition
Common optimization of adaptive preprocessing units and a neural network during the learning period. Application in EEG pattern recognition
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DOI：
10.1016/s0893-6080(97)00033-6
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发表时间：
1997-08-01
期刊：
影响因子：
7.8
通讯作者：
Griessbach, G
中科院分区：
文献类型：
--
作者：
Galicki, M;Witte, H;Griessbach, G
在本研究中，提出了神经网络（多层感知器）和自适应预处理单元同时训练的方案。这种合作使网络能够影响预处理，从而改变特征空间中模式向量的位置。因此，在学习过程中，网络试图找到一个很好的模式类分离，从而导致整个学习过程的收敛。制定该策略是为了使有效的新生儿脑电图监测成为可能。将本文提出的方法与已知的神经网络学习策略进行比较，表明需要将其用作替代学习过程。讨论了整个系统的收敛性。1997爱思唯尔科学有限公司
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. (C) 1997 Elsevier Science Ltd.