Prediction of the product formation with the AdaBoost algorithm in bioprocesses
by Lei Cui; Zhifeng Wang; Tao Xu; Haihui Song
International Journal of System Control and Information Processing (IJSCIP), Vol. 2, No. 2, 2017

Abstract: The state variables such as product formation could provide important information for the optimisation of fermentation processes. Since the kinetic modelling is difficult for bioprocesses, the product formation is predicted by integrating support vector machine (SVM) with the AdaBoost algorithm. The AdaBoost algorithm is used for adaptively boosting the performance of SVM weak learners. The prediction approach is tested by using 2-keto-L-gulonic acid (2-KGA) cultivation as an example. The validation results using the data from industrial 2-KGA cultivation demonstrate that the prediction approach has good generalisation performance and noise tolerance.

Online publication date: Mon, 12-Feb-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of System Control and Information Processing (IJSCIP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com