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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.
| Autorius: | Li Zhu, Xianglin Zhu, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 112 |
| ISBN-10: | 6204207482 |
| ISBN-13: | 9786204207483 |
| Formatas: | 220 x 150 x 7 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Soft Sensor Modeling Using Machine Learning for Fermentation Process: Taking the Marine Protease Fermentation Process as an Example“
