Abstract
Global modeling is a common approach to the problem of learning dynamical input-output mappings. It consists in fitting a single regression model, starting from the whole set of input and output measurements. On the other side of the spectrum, the local modeling approach segments the input space into several localized partitions (usually, Voronoi cells) and a number of specialized regression models are fit over each partition. Regional modeling stands in between the global and local approach. Firstly, the input space is indeed divided into partitions (as in local modeling), then partitions are merged into larger regions over which the regression models are built. In this paper, we extend the regional modeling approach through the use of robust regression, a statistical framework that better handles outliers and deviation of residuals from gaussianity. The approach is validated using two benchmark problems in system identification and its performance compared to those achieved by standard global and local models.
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de Souza Junior, A.H., Corona, F., Barreto, G.A. (2013). Robust Regional Modeling for Nonlinear System Identification Using Self-Organizing Maps. 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_22
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DOI: https://doi.org/10.1007/978-3-642-35230-0_22
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