一种基于D-S 和ARIMA 的多模型软测量方法

针对传统软测量方法存在的预测性能差、融合能力低等缺点, 提出一种基于证据理论(D-S) 合成规则和差分自回归滑动平均(ARIMA) 模型的多模型软测量方法. 首先利用自适应模糊核聚类方法和最小二乘支持向量机建立多个子模型; 然后利用D-S 合成规则构造的概率分配函数作为权值因子, 对子模型输出进行融合以得到多模型的输出; 最后结合ARIMA 模型对静态多模型输出进行动态校正. 仿真研究与工业应用的结果表明, 所提出的方法具有良好的预测性能和融合能力.

A multi-model soft sensing method based on D-S and ARIMA model

There are some disadvantages in the traditional model algorithm for the soft sensor, such as low predictive accuracy and poor fusion ability. Therefore, a multi-model soft sensor algorithm is proposed based on the D-S rule and difference autoregressive moving average(ARIMA) model. Firstly, the adaptive fuzzy kernel clustering method(AFKCM) and least squares support vector machine(LS-SVM) are used to establish multiple sub-models. Then the output of the soft sensor is obtained through the fusion of the sub-models based on the weight factor calculated by D-S rules. The ARIMA model is used to realize the dynamic correction to the static multi-model output. Simulation results and industry application indicate that, comparing with the traditional soft sensor, the proposed method has better predictive performance and fusion ability.