基于高斯过程的软测量建模

GP-based Soft Sensor Modeling

软测量技术在工业过程中得到了广泛的应用,其核心问题就是建立软测量模型。提出了一种基于高斯过程的软测量建模方法,高斯过程是一种有着概率意义的核学习机,在不牺牲性能的条件下,与人工神经网络和支持向量机相比具有实现简单的特点,理论分析和仿真研究表明了高斯过程在软测量建模中的优越性。

Soft sensors are wildly employed as an effective alternative for physical sensors in industrial processes. And the key problem is to set the model of soft sensors. We propose a novel modeling approach using Gaussian processes(GP). GPs are probabilistic kernel machines. Theoretic analysis and simulation experiment show that GP-based soft sensor is moderately simple to implement and use without loss of performance compared with artificial neural networks (ANN) and support vector machines (SVM), which lays solid basis for advanced control system.