文章基本信息

标题：Weighted Stochastic Gradient Identification Algorithms for ARX models *
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作者：Ai-Guo Wu ; Rui-Qi Dong ; Fang-Zhou Fu 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：28
页码：1076-1081
DOI：10.1016/j.ifacol.2015.12.274
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, weighted stochastic gradient (WSG) algorithms for ARX models are proposed by modifying the standard stochastic gradient identification algorithms. In the proposed algorithms, the correction term is a weighted term of the correction terms of the standard SG algorithm in the current and last recursive steps. In addition, a latest estimation based WSG (LE-WSG) algorithm is also established. The convergence performance of the proposed LE-WSG algorithm is then analyzed. It is shown by a numerical example that both the WSG and LE-WSG algorithms can possess faster convergence speed and higher convergence precision compared with the standard SG algorithms if the weighting factor is appropriately chosen.
关键词：KeywordsParameter estimationweighted stochastic gradient algorithmsconvergence performance