Soft Sensor Modeling Using Machine Learning for Fermentation Process
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Soft Sensor Modeling Using Machine Learning for Fermentation Process
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Description
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.
Product details
- Type:
- Book
- Binding:
- Paperback
- Languages:
-
Original:
English
- Number of Pages:
-
112
- Publisher:
-
LAP LAMBERT Academic Publishing
- Publication Date:
-
2021-09-30
- ISBN / EAN:
-
9786204207483
- Weight:
-
185 g
- Dimensions:
-
15/22/1 cm
- GPSR Manufacturer Reference:
-
[email protected]
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