| Autor: |
Weixing Liu, Yunjie Bai, Chun Zhang, Zijing Wang, Aimin Yang, Mingyu Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Alexandria Engineering Journal, Vol 106, Iss , Pp 505-516 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1110-0168 |
| DOI: |
10.1016/j.aej.2024.08.069 |
| Popis: |
In addressing the complexity, limited information, and dynamic spatiotemporal characteristics encountered in predicting pellet strength with traditional methods, this study proposes a novel prediction model for the strength of fusible pellets, developed on a Particle Swarm Optimization Deep Fuzzy Neural Network (PSO-DFNN). Initially, the model is constructed by observing and extracting fractal features of the microstructure of pellet ore. Subsequently, the fuzzy system is utilized to partition the spatiotemporal data and generate multi-layer fuzzy rules, thus constructing a deep fuzzy neural network. Lastly, the Particle Swarm Optimization algorithm is employed to optimize the fuzzy membership rule weights, achieving precise prediction of pellet strength. The results indicate a Mean Absolute Error (MAE) of 3.7218 and a Symmetric Mean Absolute Percentage Error (SMAPE) of 3.72 % when predicting pellet strength during the pellet roasting drying stage. The PSO-DFNN model exhibits high prediction accuracy, meeting the needs for pellet strength prediction and providing a more reliable basis for decision-making in the production process. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
