卡尔曼滤波器在软测量建模数据处理中的应用

Application of Kalman Filter in Data Processing of Soft Measurement Modeling

为了提高模型的精确度,建模分析得到的数据需要经过处理,以进一步消除噪声并降低随机因素的影响。利用卡尔曼滤波器对某石化厂支持向量机模型进行校正,达到了预期效果。仿真实验表明:卡尔曼滤波器在测量数据处理中有明显的去噪效果,在软测量模型校正中应用此方法,可以有效提高模型的精确度和可靠性。

In order to improve model accuracy,the data from modeling analysis has to be processed so as to further eliminate noise and to reduce the influence of random factors.The kalman filter was used to check the support vector machine model in a petrochemical plant and the desired results were achieved.The simulation experiment indicates that kalman filter have obvious denoising effect in the measurement data processing.Using this method in the soft measurement model correction can effectively improve the accuracy and reliability of the model.