|Table of Contents|

Soft sensor modeling for penicillin fermentation process based onadaptive weighted least squares support vector machine(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年01期
Page:
100-
Research Field:
Publishing date:

Info

Title:
Soft sensor modeling for penicillin fermentation process based onadaptive weighted least squares support vector machine
Author(s):
Zhao ChaoLi JunDai KunchengWang Guiping
School of Chemical Engineering,Fuzhou University,Fuzhou 350108,China
Keywords:
weighted least squares support vector machines penicillin fermentation process normal distribution function chaos differential evolution simulated annealing soft sensor model
PACS:
TP181
DOI:
10.14177/j.cnki.32-1397n.2017.41.01.014
Abstract:
The presence of outliers in sample data can corrupt the model’s performance,giving undesirable results.A novel adaptive weighted least squares support vector machine(AWLS-SVM)regression method is presented for modeling of penicillin fermentation process.In AWLS-SVM,least square support vector machine regression is employed for the sample data to develop model and obtain the sample datum fitting error.According to the fitting error,the adaptive sample weights are obtained via the proposed improved normal distribution weighted scheme.The hybrid chaos differential evolution simulated annealing(CDE-SA)algorithm is proposed to obtain the optimal parameters of the model.The simulation experiment results show that the outliers influencing on the models performance is eliminated in AWLS-SVM,and that the prediction performance is better than those of least squares support vector machine(LS-SVM)and weighted least squares support vector machine(WLS-SVM)method.The AWLS-SVM is applied to develop the soft sensor model for penicillin fermentation process,and the satisfactory result is obtained.

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Last Update: 2017-02-28