Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network^①

来源期刊：中南大学学报(英文版)2005年第1期

论文作者：桂卫华 李勇刚 王雅琳

文章页码：88 - 92

Key words：principal component analysis; multiple neural network; soft sensor; ratio of soda to aluminate; generalization ability

Abstract: Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.