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Soft-sensor development for fed-batch bioreactors using support vector regression

Paper ID Volume ID Publish Year Pages File Format Full-Text
5079 283 2006 15 PDF Available
Title
Soft-sensor development for fed-batch bioreactors using support vector regression
Abstract

In the present paper, a state-of-the-art machine learning based modeling formalism known as “support vector regression (SVR)”, has been introduced for the soft-sensor applications in the fed-batch processes. The SVR method possesses a number of attractive properties such as a strong statistical basis, convergence to the unique global minimum and an improved generalization performance by the approximated function. Also, the structure and parameters of an SVR model can be interpreted in terms of the training data. The efficacy of the SVR formalism for the soft-sensor development task has been demonstrated by considering two simulated bio-processes namely, invertase and streptokinase. Additionally, the performance of the SVR based soft-sensors is rigorously compared with those developed using the multilayer perceptron and radial basis function neural networks. The results presented here clearly indicate that the SVR is an attractive alternative to artificial neural networks for the development of soft-sensors in bioprocesses.

Keywords
Artificial neural networks; Bioreactor; Soft-sensors; Support vector regression; Multilayer perceptron; Radial basis function network
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biochemical Engineering Journal - Volume 27, Issue 3, January 2006, Pages 225–239
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us