Results 211 to 220 of about 124,023 (246)

Design and Iterative Development of Serious Exergames for Children With Autism Spectrum Disorder: Formative Multiple-Case Pilot Study.

JMIR Serious Games
Kim W, Seong M, Kim S.
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Iterative learning identification

Proceedings of the 36th IEEE Conference on Decision and Control, 2002
An iterative learning identification method is proposed for curve identification problems. The basic idea is to convert the curve identification problem into an optimal tracking control problem. The measured trajectories are regarded as the desired trajectories to be optimally tracked and the curve to be identified is taken as a virtual control ...
null Yangquan Chen, null Changyun Wen, null Huifang Dou, null Mingxuan Sun   +3 more
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Iterative Identification of Hammerstein Systems

Automatica, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liu, Yun, Bai, Er-Wei
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Improve Recursive Identification using Multi-Iteration

IFAC Proceedings Volumes, 2009
A multi-iteration recursive identification algorithm is proposed and studied. The algorithm performs multiple iterations for each data sample in order to improve the accuracy of the estimate. The algorithm is worked out for an ARMAX model using Gauss Newton method and pseudo-linear regression.
Zhang, Xiangping, Zhu, Y., Bosch, van den, P.P.J.   +2 more
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Iterative Identification of Feedforward Controllers for Iterative Learning Control

IFAC Proceedings Volumes, 2009
Abstract One the the main disadvantages to the standard iterative learning control (ILC) problem is that the learning filter is static and does not update based on the current iteration information. This means that while the learning control is updating the feedforward control input, the learning controller itself is not updating.
Hoday Stearns, Benjamin Fine, Masayoshi Tomizuka   +2 more
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Iterative learning identification using quantized observations

2017 6th Data Driven Control and Learning Systems (DDCLS), 2017
This paper develops a novel iterative learning parameter identification algorithm for a class of single parameter systems with multi-threshold quantized observations. The identification algorithm is constructed along the iteration axis and it can incorporate the parameter identification ability and the learning ability to deal with unknown time-varying
Xuhui Bu, Jian Liu, Zhongsheng Hou
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