Title :
Soft sensor modeling method based on k-nearest neighbor and RBF neural network
Author :
Weijun Zhang ; Hongbo Gao
Author_Institution :
Sch. of Mater. & Metall., Northeastern Univ., Shenyang, China
Abstract :
A soft sensor modeling method based on k-nearest neighbor and RBF neural network is presented to diminish the effects of outliers on the developed soft sensor model. Firstly, the anomaly degree of each modeling data pairs is calculated by using the k-nearest neighbor algorithm. Then, the weight of each modeling data pairs is determined according to the calculated anomaly degrees. Lastly, a soft sensor model is developed by using RBF neural network with weighted training error. Simulation is performed using functional data and production data from Nosiheptide fermentation process, and the simulation results show the effectiveness of the presented approach.
Keywords :
fermentation; pattern clustering; radial basis function networks; Nosiheptide fermentation process; RBF neural network; anomaly degree calculation; functional data; k-nearest neighbor; modeling data pair weight; outliers; production data; radial basis function networks; simulation results; soft sensor modeling method; weighted training error; Analytical models; Biological system modeling; Biomass; Data models; Estimation; Neural networks; Training; RBF neural network; k-nearest neighbor; modeling; outlier; soft sensor;
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-2130-4
DOI :
10.1109/ICNC.2012.6234583
