Table 3.
Performance comparison of different type BP neural networks.
| Size of training set | Integer-order BP neural networks | Fractional-order BP neural networks | Integer-order BP neural networks with L2 regularization | Fractional-order BP neural networks with L2 regularization | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Training Accuracy | Testing Accuracy | Training Accuracy | Testing Accuracy | Training Accuracy | Testing Accuracy | Training Accuracy | Testing Accuracy | Improvement relative to IOBP | Improvement relative to FOBP | |
| 10000 | 98.41% | 89.87% | 98.48% | 90.31% | 98.45% | 93.35% | 98.43% | 93.95% | 4.54% | 4.03% |
| 20000 | 98.81% | 92.28% | 98.84% | 92.34% | 98.75% | 95.09% | 98.79% | 95.13% | 3.09% | 3.02% |
| 30000 | 98.95% | 93.38% | 99.05% | 93.50% | 98.92% | 95.15% | 98.88% | 95.62% | 2.40% | 2.27% |
| 40000 | 99.05% | 93.83% | 99.01% | 93.92% | 98.96% | 95.63% | 98.95% | 95.83% | 2.13% | 2.03% |
| 50000 | 99.20% | 94.55% | 99.17% | 94.56% | 99.11% | 96.08% | 99.15% | 96.45% | 2.01% | 2.00% |
| 60000 | 99.17% | 94.87% | 99.20% | 95.00% | 99.13% | 96.51% | 99.17% | 96.70% | 1.93% | 1.79% |
We use the following formula to calculate improvement: improvement of A compared with B = (A-B)÷B.