Abstract
The paper describes and compares some applications of neuro- fuzzy (NF) systems to estimate the speed of a train from the measure- ment of the velocity of two axles in any wheel/rail adhesion conditions. All the presented NF approaches outperforms the firstly designed crisp algorithm in terms of computational burden and some of them achieve also a significative performance improvement, by demonstrating their capability of learning from rough data.
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Colla, V., Vannucci, M., Allottay, B., Malvezziy, M. (2003). Estimation of train speed via neuro–fuzzy techniques. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_63
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DOI: https://doi.org/10.1007/3-540-44869-1_63
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