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

• المؤلفون الرئيسيون: Ni, Wangdong, Tan, Soon Keat, Ng, Wun Jern, Brown, Steven D.
• مؤلفون آخرون: School of Civil and Environmental Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2013
• الموضوعات: DRNTU::Engineering::Civil engineering::Water resources
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/105944; http://hdl.handle.net/10220/16749; http://dx.doi.org/10.1021/ie201898a
• المؤسسة: 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...