高斯过程
模型预测控制
计算机科学
高斯分布
概率逻辑
过程控制
水准点(测量)
过程(计算)
差异(会计)
算法
机器学习
人工智能
控制(管理)
会计
地理
业务
物理
操作系统
量子力学
大地测量学
作者
Juš Kocijan,Roderick Murray-Smith,Carl Edward Rasmussen,Agathe Girard
标识
DOI:10.23919/acc.2004.1383790
摘要
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.
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