| Título: | The prediction of Granulating Effect Based on BP Neural Network |
| Autores: |
Li, Fang; Chongqing University Wu, Kaigui; Chongqing University Zhao, Guanyin; Chongqing University |
| Fecha: | 2013-12-29 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: | No aplica |
| Descripción: | During the granulation process of Iron ore sinter mixture, there are many factors affect the granulating effect, such as chemical composition, size distribution, surface feature of particle, and so on. Some researchers use traditional fitting calculation methods like least square method and regression analysis method to predict granulation effects, which exists big error. In order to predict it better, we build improved BP (Back propagation) neural network model to carry out data analysis and processing, and then obtain better effect than traditional fitting calculation methods. |
| Idioma: | No aplica |