- Tytuł:
- Evolutionary data driven modelling and many objective optimization of non linear noisy data in the blast furnace iron making process
- Autorzy:
-
Mahanta, Bashista Kumar
Chakraborti, Nirupam - Powiązania:
- https://bibliotekanauki.pl/articles/29520226.pdf
- Data publikacji:
- 2021
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
deep learning
reference vector
neural net
genetic programming
blast furnace - Opis:
- Optimization of process parameters in modern blast furnace operation, where both control and accessing large data set with multiple variables and objectives is a challenging task. To handle such non-linear and noisy data set deep learning techniques have been used in recent time. In this study an evolutionary deep neural network algorithm (EvoDN2) has been applied to derive a data driven model for blast furnace. The optimal front generated from deep neural network is compared against the optimal models developed from bi-objective genetic programming algorithm (BioGP) and evolutionary neural network (EvoNN). The optimization process is applied to all the training models by using constraint based reference vector evolutionary algorithm (cRVEA).
- Źródło:
-
Computer Methods in Materials Science; 2021, 21, 3; 163-175
2720-4081
2720-3948 - Pojawia się w:
- Computer Methods in Materials Science
- Dostawca treści:
- Biblioteka Nauki