基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量

间歇过程具有较强的非线性，多阶段、慢时变及批次间存在变化，采用单一预测模型不能反映间歇过程的多阶段特性及阶段间过渡特性。提出一种基于Gath-Geva聚类和核极限学习机（kernel extreme learning machine，KELM）的多模型软测量方法。首先采用主成分分析（principal component analysis，PCA）对输入做特征提取，然后利用Gath-Geva算法对间歇过程进行多阶段工况划分，根据生产工况特性划分为不同的操作阶段后，分别建立局部KELM模型。对任一待预测样本，分别计算其对应各局部模型的预测值，最后采用贝叶斯集成，将其隶属于各局部模型的模糊隶属度作为权重和预测值融合得到最终预测值。以青霉素发酵数据进行实验测试，结果表明所提多模型算法相较于单一模型，具有更高的预测精度。

Soft sensors for multi-stage batch processes based on Gath-Geva algorithm and kernel extreme learning machine

Because batch processes have strong non-linearity, multi-stage, slow time-evolution, and batch-to-batch variation, conventional single prediction model cannot effectively capture characteristics of multi-stage and inter-stage transition. A novel multi-model soft sensor method was proposed on the basis of Gath-Geva clustering and kernel extreme learning machine (KELM). First, principal component analysis (PCA) was used to extract features of input variables. Then, Gath-Geva algorithm was used to classify different operating stages of the batch process and local KELM model was built for each operating stage. For a query sample, every local KELM predictions were calculated and final predictions were obtained by integrating fuzzy membership of each local KELM as weight and its corresponding prediction value. The numeric simulation results on data of penicillin fermentation show that this multi-model approach has more accurate prediction than single model.