Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing

• Main Authors: Ni, Wangdong, Tan, Soon Keat, Ng, Wun Jern, Brown, Steven D.
• 其他作者: School of Civil and Environmental Engineering
• 格式: Article
• 語言: English
• 出版: 2013
• 主題: DRNTU::Engineering::Civil engineering::Water resources
• 在線閱讀: https://hdl.handle.net/10356/105944; http://hdl.handle.net/10220/16749; http://dx.doi.org/10.1021/ie201898a
• 機構: Nanyang Technological University

The characteristics of nonlinearity and time-varying changes in most industrial processes usually cripple the predictive performance of conventional soft sensors. In this article, moving-window Gaussian process regression (MWGPR) is proposed to effectively capture the process dynamics and to model n...