期刊名称：International Journal of Computer Science & Applications
印刷版ISSN：0972-9038
出版年度：2007
卷号：IV
期号：III
页码：45-56
出版社：Technomathematics Research Foundation
摘要：In this paper, we propose a novel neural architecture that adaptively learns an input-output mapping using both
supervised and non-supervised trainings. This neural architecture consists of a combination of an ART2 (Adaptive
Resonance Theory) neural network and recurrent neural networks. For this end, we developed an Extended Kalman
Filter (EKF) based training algorithm for the involved recurrent neural networks. The proposed ART2/EKF neural
network is inspired in the visual cortex and the brain mechanisms. More precisely, the non-supervised ART2 neural
network is used to coordinate specialized recurrent neural networks in a specific input space domain. Our aim is to
design a neural system that learns in real time a new input pattern without retraining the neural network with the whole
training set. The proposed neural architecture is used to adaptively predict the traffic volume in a computer network.
We verify that the ART2/EKF is capable of finding patterns in the traffic time series as well as to obtain the transmission
rate that should be made available in order to avoid byte losses in a computer network link.
