Figure 1:
Flow chart of the gas concentration prediction algorithm. SSA, sparrow search algorithm; SVR, support vector regression.
Table 1:
Initial parameter table
| Parameter | Value |
|---|---|
| Maximum iterations | 200 |
| Population size | 100 |
| Optimization parameter number | 2 |
| Upper limit of optimization parameter | 2−10 |
| Lower limit of optimization parameter | 210 |
| Cross-check the number of folds | 5 |
Table 2:
Model parameter settings
| Algorithm | Parameter | Value range | Optimum value |
|---|---|---|---|
| SVR | C | / | 10 |
| g | 0.5 | ||
| SSA-SVR | Alarm value | [0.5, 1] | 0.8 |
| Safety threshold | [0, 1] | 0.2 | |
| TSSA-SVR | Alarm value | [0.5, 1] | 0.8 |
| Safety threshold | [0, 1] | 0.2 |
Table 3:
Quantitative identification results of mixed gases
| Model | RMSE | MAE | R2 | Acc (%) |
|---|---|---|---|---|
| SVR | 2.626 | 2.336 | 0.095 | 81.80 |
| SSA-SVR | 1.982 | 1.485 | 0.375 | 87.43 |
| GA-SVR | 1.892 | 1.367 | 0.482 | 89.67 |
| PSO-SVR | 1.765 | 1.220 | 0.512 | 90.36 |
| TSSA-SVR | 0.286 | 0.425 | 0.896 | 94.47 |
Figure 2:
Fitness change curves of two different algorithms. SSA, sparrow search algorithm.
Figure 3:
The absolute error of the two algorithm models. SSA, sparrow search algorithm; SVM, support vector machine.