关键词: 自组织映射网络 , 主元分析 , 关联向量机 , 软测量 , 对羧基苯甲醛

Abstract: A hybrid modeling method for the complex process was proposed．The process，which was highly nonlinearity，had interacting independent variables， and had process data belonged to different categories． This method combined self－organizing map network （SOM network），principal component analysis （PCA），and relevance vector machine （RVM）．First，
divided the sample datum into several subspaces which had the same patterns with the utilizing of SOM，the pattern space separation was realized．Then derived the principal components based on the modeling samples of each subspace，defined the optimal principal component numbers based on prediction ability，and filtered the redundancy．At last，the principal component
of each subspace was applied as the input of RVM，building the models individually ，and the classifying modeling was realized based on the separated of sample pattern spaces．The simulation results and the application in 4－carboxybenzaldehyde content soft sensor of pureed terephthalic acid production show that the accuracies of regressing and predicting of SOM－PCA －RVM model is better than both RVM model and PCA － RVM model．

Key words: SOM network , Principal component analysis , Relevance vector machine , Soft sensor , 4－Carboxybenzaldehyde