| 基于高斯过程的软测量建模 |
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| 引用本文： | 熊志化,张继承,邵惠鹤.基于高斯过程的软测量建模[J].系统仿真学报,2005,17(4):793-794,800. |
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| 作者姓名： | 熊志化 张继承 邵惠鹤 |
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| 作者单位： | 1. 上海交通大学自动化研究所,上海,200030
2. 东华大学信息学院,上海,200051 |
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| 基金项目： | 国家高技术研究发展计划(863计划)重点项目(2002AA412010) |
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| 摘 要： | 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。
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| 关 键 词： | 高斯过程 软测量 凝固点 建模 支持向量机 人工神经网络 |
| 文章编号： | 1004-731X(2005)04-0793-02 |
GP-based Soft Sensor Modeling |
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| XIONG Zhi-hua,ZHANG Ji-cheng,SHAO Hui-he.GP-based Soft Sensor Modeling[J].Journal of System Simulation,2005,17(4):793-794,800. |
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| Authors: | XIONG Zhi-hua ZHANG Ji-cheng SHAO Hui-he |
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| Abstract: | 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. |
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| Keywords: | Gaussian processes soft sensor freezing point modeling support vector machines artificial neural networks |
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