基于高斯过程和支持向量机的软测量建模比较及应用研究
Comparison and Application Research on Soft Sensor Modeling Based on Gaussian Processes and Support Vector Machines
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摘要: 给出了基于高斯过程和支持向量机的软测量建模方法，在不牺牲性能的条件下，高斯过程与支持向量机相比，是一种有着概率意义的核学习机，同时它更容易实现，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．Abstract: The comparison between Gaussian processes(GP)and s upport vector machines(SVM)is presented.Theoretical analysis and simulation experiment show that GP-based soft sensor is a probabilistic kernel machine and moderately simple to implement and use without loss of performance compared with SVM,which lays solid base for advanced control system.
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