Abstract
In this paper, we introduce the Gamma Growing Neural Gas (γ-GNG) model for temporal sequence processing. The standard GNG is merged with a context descriptor based on a short term memory structure called Gamma memory. When using a single stage of the Gamma filter, the Merge GNG model is recovered. The γ-GNG model is compared to γ-Neural Gas, γ-SOM, and Merge Neural Gas, using the temporal quantization error as a performance measure. Simulation results on two data sets are presented: Mackey-Glass time series, and Bicup 2006 challenge time series.
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Estévez, P.A., Hernández, R. (2011). Gamma-Filter Self-Organizing Neural Networks for Time Series Analysis. In: Laaksonen, J., Honkela, T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21566-7_15
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DOI: https://doi.org/10.1007/978-3-642-21566-7_15
Publisher Name: Springer, Berlin, Heidelberg
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