Abstract
In this chapter we deal with learning (optimization) methods used in modeling and identification of complex nonlinear systems. We treat methods that enable estimation of model parameters and, possibly, identification of the structure of the models from the measured data (samples).
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Karer, G., Škrjanc, I. (2013). Unsupervised Learning Methods for Identification of Complex Systems. In: Predictive Approaches to Control of Complex Systems. Studies in Computational Intelligence, vol 454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33947-9_5
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DOI: https://doi.org/10.1007/978-3-642-33947-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33946-2
Online ISBN: 978-3-642-33947-9
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