Parameter Estimation of Least Squares Collocation
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- regularizer matrix; parametric estimation; least squares collocation; smoothing parameter
In this paper, based on penalized least squares, the penalized weighted sum of squares is set up, we deduce the calculation method of a positively definite regularity matrix and obtain the corresponding results in least squares collocation model. According to the foundational properties of the random errors, we study how to choice a reasonable smoothing parameter and regularizer matrix. Concepts of confidence region in probability and method of region estimation are used. The adaptation of this technique is more flexible than the traditional model.
- © 2016, 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 - Lihong Jin PY - 2016/07 DA - 2016/07 TI - Parameter Estimation of Least Squares Collocation BT - Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering PB - Atlantis Press SP - 184 EP - 188 SN - 2352-5401 UR - https://doi.org/10.2991/mcae-16.2016.44 DO - https://doi.org/10.2991/mcae-16.2016.44 ID - Jin2016/07 ER -