Fast just-in-time-learning recursive multi-output LSSVR for quality prediction and control of multivariable dynamic systems

Aiming at quality prediction and control of blast furnace (BF) ironmaking process characterized by complicated nonlinear time-varying dynamics, this paper proposes a just-in-time-learning (JITL) recursive multi-output least squares support vector regression (JITL-R-M-LSSVR) algorithm with fast nonli...

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Vydáno v:	Engineering applications of artificial intelligence Ročník 100; s. 104168
Hlavní autoři:	Zhou, Ping, Chen, Weiqi, Yi, Chengming, Jiang, Zhaohui, Yang, Tao, Chai, Tianyou
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.04.2021
Témata:	Blast furnace ironmaking process Incremental learning Just-in-time-learning (JITL) Local learning nonlinear modeling Model verification Multi-output LSSVR (M-LSSVR) Nonlinear predictive control Incremental learning Blast furnace ironmaking process Just-in-time-learning (JITL) Multi-output LSSVR (M-LSSVR) Model verification Local learning nonlinear modeling Nonlinear predictive control
ISSN:	0952-1976, 1873-6769
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