Hybrid recursive least squares algorithm for online sequential identification using data chunks

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• 作者：Ning Wang^a ; ^{n.wang.dmu.cn@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jing-Chao Sun^a ; ^bAuthor Vitae ; Meng Joo Er^cAuthor Vitae ; Yan-Cheng Liu^aAuthor Vitae
• 关键词：Hybrid recursive least squares (HRLS) ; Online sequential identification ; Recursive orthogonal least squares (ROLS) ; Single-hidden-layer feedforward network (SLFN)
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：22 January 2016
• 年：2016
• 卷：174
• 期：part_PB
• 页码：651-660
• 全文大小：816 K

文摘

In this paper, a hybrid recursive least squares (HRLS) algorithm for online identification using sequential chunk-by-chunk observations is proposed. By employing the optimization-based least squares (O-LS), the HRLS can be initialized with any chunk of data samples and works successively in two recursive procedures for updating the inverse matrix with minimal dimension and least rank-deficiency, and thereby contributing to fast and stable online identification. Since norms of the output weight and training errors are minimized simultaneously, the HRLS achieves high accuracy in terms of both generalization and approximation. Simulation studies and comprehensive comparisons demonstrate that the HRLS is numerically more stable and superior to other algorithms in terms of accuracy and speed.