Abstract
Prediction and modeling of signal are tasks that can be done by many methods among which the neural networks have an important place due to the fact that it is data driven method that doesn´t require extensive understanding of the process. This paper presents a new type of neural network that was tested on the modeling of EEG signal. The performance of this network was compared to traditional NN methods. The novelty of this network consists in the fact that each neuron is processing its learning as an independent unit, without any higher process or structure. The learning rule can be simply described as ´improving the significance of the neuron for the rest of the network´. The growth of computational power in recent years opens new possibilities to the use of neural networks in artificial intelligence.
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Ruzek, M. (2013). Modeling of EEG Signal with Homeostatic Neural Network. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_18
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DOI: https://doi.org/10.1007/978-3-319-00542-3_18
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00541-6
Online ISBN: 978-3-319-00542-3
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