Adaptive Soft-sensor Modeling Algorithm Based on FCMISVM and Its Application in PX Adsorption Separation Process

To overcome the problem that soft sensor models cannot be updated with the process changes, a soft sensor modeling algorithm based on hybrid fuzzy c-means （FCM） algorithm and incremental support vector machines （ISVM） is proposed. This hybrid algorithm FCMISVM includes three parts： samples clustering based on FCM algorithm, learning algorithm based on ISVM, and heuristic sample displacement method. In the training process, the training samples are first clustered by the FCM algorithm, and then by training each clustering with the SVM algorithm, a sub-model is built to each clustering. In the predicting process, when an incremental sample that represents new operation information is introduced in the model, the fuzzy membership function of the sample to each clustering is first computed by the FCM algorithm. Then, a corresponding SVM sub-model of the clustering with the largest fuzzy membership function is used to predict and perform incremental learning so the model can be updated on-line. An old sample chosen by heuristic sample displacement method is then discarded from the sub-model to control the size of the working set. The proposed method is applied to predict the p-xylene （PX） purity in the adsorption separation process. Simulation results indicate that the proposed method actually increases the model＇s adaptive abilities to various operation conditions and improves its generalization capability.

为了克服软传感器模型不能与这个过程被更新的问题，变化，基于算法和增长支持向量用机器制造的混合模糊 c 工具(FCM )(ISVM ) 为算法建模的一个软传感器被建议。这混合算法 FCMISVM 包括三部分：取样基于 FCM 算法聚类，学习基于 ISVM，和启发式的样品排水量方法的算法。在训练过程，训练样品被与 SVM 算法聚类的 FCM 算法，然后 by training 各个首先聚类，一个亚模型被造到每个聚类。在预言的进程，当表示新操作信息的一件增长样品在模型被介绍时，到每个聚类的样品的模糊会员函数是首先由 FCM 算法计算了。然后，与最大的模糊会员功能聚类的一个相应 SVM 亚模型被用来预言并且表现增长学习因此模型能被更新联机。启发式的样品排水量方法选择的一件旧样品然后从亚模型被丢弃控制工作集合的尺寸。建议方法被使用在吸附分离过程预言 p 二甲苯(PX ) 纯净。模拟结果显示建议方法实际上增加模型的适应能力到各种各样的操作条件并且改进它的归纳能力。