Research Article
Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion
Table 4
Predictive error comparison of soft-sensor model.
| Predicted variables | Predictive method | NRMSE | MSE | MAPE |
| Concentrate grade (%) | ESN | 0.0122 | 0.6828 | 1.0310 | GA-ESN | 0.0093 | 0.2379 | 0.6020 | PSO-ESN | 0.0089 | 0.1918 | 0.5435 | GSO-ESN | 0.0102 | 0.3562 | 0.7205 | IGSO-ESN | 0.0069 | 0.0681 | 0.3306 |
| Flotation recovery rate (%) | ESN | 0.0096 | 1.0195 | 0.8807 | GA-ESN | 0.0075 | 0.3593 | 0.5468 | PSO-ESN | 0.0078 | 0.4158 | 0.5929 | GSO-ESN | 0.0088 | 0.6626 | 0.7482 | IGSO-ESN | 0.0060 | 0.1467 | 0.3530 |
|
|