Abstract
In this paper, we investigate algorithms for constructing experimental data dependence based on sequential processing of the points one by one. Four algorithms are reviewed, comparative analysis for different basis functions, a level of noise and other options is made. In addition to static data, there was an investigation of dynamic data case. The sine with variable frequency is used as an approximative function. Numerical experiments led to the conclusions about the comparative efficiency of algorithms and basis functions. The recommendations for the use of the algorithms are given.
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Tarasenko, F.D., Tarkhov, D.A. (2016). Basis Functions Comparative Analysis in Consecutive Data Smoothing Algorithms. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_55
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DOI: https://doi.org/10.1007/978-3-319-40663-3_55
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