Abstract
An important assumption in the global approach to system identification is the homogeneity of observed time series from statistical point of view. A violation of this assumption leads to biased estimated parameters and a low quality of the model.
This paper addresses the task of change-point detection by means of Artificial Neural Networks (ANN). The focus lies on the appropriate design of ANN by specifying the inputs, outputs and the necessary number of hidden nodes for an error-free classification of the data.
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Neuner, H. (2015). Design of Artificial Neural Networks for Change-Point Detection. In: Kutterer, H., Seitz, F., Alkhatib, H., Schmidt, M. (eds) The 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS'11). International Association of Geodesy Symposia, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-10828-5_20
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DOI: https://doi.org/10.1007/978-3-319-10828-5_20
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