Abstract
In this paper, we investigate the properties of the Gamma Growing Neural Gas (γ-GNG) model for the analysis of nonlinear time series. This model includes a temporal context descriptor based on a short term memory structure called Gamma memory. It is shown that γ-GNG can approximately reconstruct the space-state, and filter out additive noise. Simulation results on two data sets are presented: Lorenz system and NH3-FIR Laser time series.
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Estévez, P.A., Vergara, J.R. (2013). Nonlinear Time Series Analysis by Using Gamma Growing Neural Gas. In: Estévez, P., Príncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_21
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DOI: https://doi.org/10.1007/978-3-642-35230-0_21
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