基于FCMISVM的自适应软测量建模算法及其在PX吸附分离过程中的应用

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.

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 ma-chines(ISVM)iS proposed.This hybrid algorithm FCMISVM includes three parts:samples clustering based on FCM algorithm.leaming algorithm based on ISVM.and heuristic sample displacement method.In the training process,the training samples are first clustered bv 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 bv 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.