基于改进的GA-LSSVM的软测量建模方法

Soft Sensing Based on Improved GA-LSSVM

  • 摘要: 针对工业过程中某些重要过程变量难以实现在线测量的问题,提出了一种改进的最小二乘支持向量机(IGA-LSSVM)的软测量建模方法. 该方法采用核独立分量分析(KICA)对高维数据进行特征提取,利用改进的最小二乘支持向量机进行建模. 该方法既利用了最小二乘支持向量机求解速度快的特点,又利用了自适应遗传算法强大的全局搜索能力,增强了模型的自适应性. 用该方法建立柴油凝点的软测量模型,结果表明,基于IGA-LSSVM方法建立的软测量模型具有较高的预测精度和泛化能力.

     

    Abstract: Some variables in industrial process are very difficult to measure on-line. To overcome this problem, a kind of soft sensing based on improved GA-LSSVM (IGA-LSSVM) is proposed in this research. First, KICA was used to extract main features of the data with high dimension patterns, and then an improved GA-LSSVM was established. This model not only utilizes the ability of quickly solving speed of LSSVM, but also the powerful global search performance of adaptive GA. Therefore the adaptability of LSSVM model is improved. The proposed method has been used for building soft sensing of diesel oil solidifying point. The result shows that IGA-LSSVM approach has high precision and good generalization ability.

     

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