Research Article
Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network
Table 3
Comparison on prediction among the QPSO-NN, PSO-NN, and BPNN.
| | Ratio of training set (%) | Prediction precision and ANOVA test | | BPNN (x) (%) | PSO-NN (y) (%) | QPSO-NN (z) (%) | z–y (%) | value | z–x (%) | value |
| | 40 | 80.12 | 82.87 | 89.51 | 6.64 | 8.65 × 10−7 | 9.39 | 4.13 × 10−13 | | 50 | 80.23 | 84.03 | 89.65 | 5.62 | 1.12 × 10−7 | 9.42 | 1.39 × 10−9 | | 60 | 81.26 | 84.26 | 90.11 | 5.85 | 1.03 × 10−7 | 8.85 | 1.68 × 10−11 | | 70 | 82.75 | 85.10 | 90.23 | 5.13 | 5.99 × 10−8 | 7.48 | 1.22 × 10−11 | | 80 | 83.33 | 85.80 | 91.34 | 5.54 | 3.49 × 10−5 | 8.01 | 1.26 × 10−11 | | 90 | 83.41 | 86.02 | 91.95 | 5.93 | 1.67 × 10−6 | 8.54 | 6.29 × 10−18 |
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