.png){kind=icon}
Return
Hybrid recursive least squares algorithm for online sequential identification using data chunks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.09.090.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Hybrid recursive least squares (HRLS)
Online sequential identification
Recursive orthogonal least squares (ROLS)
Single-hidden-layer feedforward network (SLFN)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
Cited Papers
ShareSaveProductivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials.png)
Desalination
OAAI
DesalinationIF0
OAAIShareSaveConstructive multi-output extreme learning machine with application to large tanker motion dynamics identificationNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSaveShareSaveShareSave
View more