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Soft Sensor Modeling Using Machine Learning for Fermentation Process: Taking the Marine Protease Fermentation Process as an Example - Softcover
Synopsis
The aim of the present book has been to develop soft sensor solutions for upstream bioprocessing and demonstrate their usefulness in improving robustness and increasing the batch-to-batch reproducibility in bioprocesses. This book study encompasses the following objectives:- To propose and compare the performance of successive projection algorithm with grey relation analysis algorithm in terms of auxiliary variables selection; - To propose and compare the performance of SPA-GWO-SVR soft sensor model with SPA-SVR model in terms of accuracy, root mean square error, coefficient determination R2;- To propose exponential decreasing inertia weight strategy with PSO algorithm that exploits search space and thus by reducing large step lengths leads the PSO towards convergence to global optima; - To propose the fuzzy c-means clustering algorithm to cluster the sample data and compare the performances of the IPSO-LSSVM soft sensor model with standard PSO-LSSVM model on selected benchmarked regression datasets in terms of accuracy, mean square error, root mean square error, and mean absolute error.
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Soft Sensor Modeling Using Machine Learning for Fermentation Process
Published by
LAP LAMBERT Academic Publishing Sep 2021, 2021
ISBN 10: 6204207482
ISBN 13: 9786204207483
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The aim of the present book has been to develop soft sensor solutions for upstream bioprocessing and demonstrate their usefulness in improving robustness and increasing the batch-to-batch reproducibility in bioprocesses. This book study encompasses the following objectives:- To propose and compare the performance of successive projection algorithm with grey relation analysis algorithm in terms of auxiliary variables selection; - To propose and compare the performance of SPA-GWO-SVR soft sensor model with SPA-SVR model in terms of accuracy, root mean square error, coefficient determination R2;- To propose exponential decreasing inertia weight strategy with PSO algorithm that exploits search space and thus by reducing large step lengths leads the PSO towards convergence to global optima; - To propose the fuzzy c-means clustering algorithm to cluster the sample data and compare the performances of the IPSO-LSSVM soft sensor model with standard PSO-LSSVM model on selected benchmarked regression datasets in terms of accuracy, mean square error, root mean square error, and mean absolute error. 112 pp. Englisch. Seller Inventory # 9786204207483
Quantity: 2 available
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Soft Sensor Modeling Using Machine Learning for Fermentation Process
Published by
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204207482
ISBN 13: 9786204207483
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 4 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zhu LiLi Zhu is a teacher at Jiangsu University, mainly engaged in microbial fermentation detection and control technology. Previously, she worked within provincial and ministerial levels. The author won two National and Provincial A. Seller Inventory # 515838074
Quantity: Over 20 available
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Soft Sensor Modeling Using Machine Learning for Fermentation Process
Published by
LAP LAMBERT Academic Publishing Sep 2021, 2021
ISBN 10: 6204207482
ISBN 13: 9786204207483
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -The aim of the present book has been to develop soft sensor solutions for upstream bioprocessing and demonstrate their usefulness in improving robustness and increasing the batch-to-batch reproducibility in bioprocesses. This book study encompasses the following objectives:- To propose and compare the performance of successive projection algorithm with grey relation analysis algorithm in terms of auxiliary variables selection; - To propose and compare the performance of SPA-GWO-SVR soft sensor model with SPA-SVR model in terms of accuracy, root mean square error, coefficient determination R2;- To propose exponential decreasing inertia weight strategy with PSO algorithm that exploits search space and thus by reducing large step lengths leads the PSO towards convergence to global optima; - To propose the fuzzy c-means clustering algorithm to cluster the sample data and compare the performances of the IPSO-LSSVM soft sensor model with standard PSO-LSSVM model on selected benchmarked regression datasets in terms of accuracy, mean square error, root mean square error, and mean absolute error.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch. Seller Inventory # 9786204207483
Quantity: 2 available
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Soft Sensor Modeling Using Machine Learning for Fermentation Process : Taking the Marine Protease Fermentation Process as an Example
Published by
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204207482
ISBN 13: 9786204207483
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The aim of the present book has been to develop soft sensor solutions for upstream bioprocessing and demonstrate their usefulness in improving robustness and increasing the batch-to-batch reproducibility in bioprocesses. This book study encompasses the following objectives:- To propose and compare the performance of successive projection algorithm with grey relation analysis algorithm in terms of auxiliary variables selection; - To propose and compare the performance of SPA-GWO-SVR soft sensor model with SPA-SVR model in terms of accuracy, root mean square error, coefficient determination R2;- To propose exponential decreasing inertia weight strategy with PSO algorithm that exploits search space and thus by reducing large step lengths leads the PSO towards convergence to global optima; - To propose the fuzzy c-means clustering algorithm to cluster the sample data and compare the performances of the IPSO-LSSVM soft sensor model with standard PSO-LSSVM model on selected benchmarked regression datasets in terms of accuracy, mean square error, root mean square error, and mean absolute error. Seller Inventory # 9786204207483
Quantity: 1 available
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Soft Sensor Modeling Using Machine Learning for Fermentation Process: Taking the Marine Protease Fermentation Process as an Example
Published by
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204207482
ISBN 13: 9786204207483
New
paperback
Seller: Mispah books, Redhill, SURRE, United Kingdom
Seller rating 4 out of 5 stars
paperback. Condition: New. New. book. Seller Inventory # ERICA82962042074826
Quantity: 1 available