基于改进仿射传播的多模型软测量建模及应用研究

Multi-model Soft Sensor Modeling and Its Application Based on Improved Affinity Propagation Algorithm

  • 摘要: 针对具有高维度、多工况特性的工业生产过程,提出一种改进仿射传播聚类(AP)的多模型软测量建模方法.首先,采用主成分分析方法和差分进化算法对传统的仿射传播聚类算法进行改进,使算法可以避免冗余信息影响的同时,还可以实现参数的寻优,得到全局最优的子数据集;然后,基于高斯过程回归建立各局部预测模型;最后,对于新来的样本,利用预测方差计算其隶属于各局部模型的后验概率,以此为权重对各局部模型进行融合,得到最终的预测输出.通过对两个标准数据集和污水处理过程数据的仿真,验证了所提方法的有效性.

     

    Abstract: For industrial processes with high dimensions and multiple modes, we propose a multi-model soft sensor modeling method based on an improved affinity propagation (AP) clustering algorithm. First, we apply the principal component analysis and differential evolution method to improving the performance of the traditional AP algorithm and to removing the influence of redundant information. The most accurate sub-datasets are obtained based on the optimized parameters. Second, we use Gaussian process regression to construct local models. Finally, we utilize the prediction variance of the new data to calculate the posterior probability, and obtain the prediction result through a combination of different local models. The effectiveness of the proposed algorithm is verified through simulation results of two benchmark datasets and a sewage treatment process.

     

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