Combined Identification and Prediction Algorithms
- 10.2991/itsmssm-17.2017.51How to use a DOI?
- Nadaraya-Watson nonparametric estimate, parametric estimate, a prior guess, regression, combined algorithm, identification, prediction, bootstrap.
In many applied problems it is required to construct a mathematical model of the dependence of output variables on input variables of the stochastic object. To solve this problem, both parametric and nonparametric approaches are used. Each of these approaches has advantages and disadvantages. In the paper, we consider combined algorithms for the identification and prediction of stochastic objects using jointly nonparametric and parametric estimates of regression.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yury Dmitriev AU - Gennady Koshkin AU - Vadim Lukov PY - 2017/12 DA - 2017/12 TI - Combined Identification and Prediction Algorithms BT - Proceedings of the IV International research conference "Information technologies in Science, Management, Social sphere and Medicine" (ITSMSSM 2017) PB - Atlantis Press SP - 244 EP - 247 SN - 2352-538X UR - https://doi.org/10.2991/itsmssm-17.2017.51 DO - 10.2991/itsmssm-17.2017.51 ID - Dmitriev2017/12 ER -