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author:

Zhao, C. (Zhao, C..) [1] | Li, J. (Li, J..) [2] | Dai, K. (Dai, K..) [3] | Wang, G. (Wang, G..) [4]

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Scopus CSCD

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. © 2017, Editorial Department of Journal of Nanjing University of Science and Technology. All right reserved.

Keyword:

Chaos differential evolution simulated annealing; Normal distribution function; Penicillin fermentation process; Soft sensor model; Weighted least squares support vector machines

Community:

  • [ 1 ] [Zhao, C.]School of Chemical Engineering, Fuzhou University, Fuzhou, 350108, China
  • [ 2 ] [Li, J.]School of Chemical Engineering, Fuzhou University, Fuzhou, 350108, China
  • [ 3 ] [Dai, K.]School of Chemical Engineering, Fuzhou University, Fuzhou, 350108, China
  • [ 4 ] [Wang, G.]School of Chemical Engineering, Fuzhou University, Fuzhou, 350108, China

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Source :

Journal of Nanjing University of Science and Technology

ISSN: 1005-9830

Year: 2017

Issue: 1

Volume: 41

Page: 100-107

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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