Table 1 Neural network approximation for the coefficients
From: A novel computational approach to approximate fuzzy interpolation polynomials
t | \(x_0(t)\) | \(x_1(t)\) | \(x_2(t)\) | \(x_3(t)\) | Error for FNN |
|---|---|---|---|---|---|
1 | 4.9018 | 6.9215 | 5.9307 | 7.9121 | 58,756.65 |
2 | 4.5321 | 6.6450 | 5.5480 | 7.6010 | 6479.790 |
3 | 4.0231 | 6.2056 | 5.1250 | 7.2212 | 1741.483 |
4 | 3.6850 | 5.8401 | 4.7851 | 6.7945 | 577.7597 |
5 | 3.2032 | 5.4001 | 4.3365 | 6.3330 | 210.8822 |
\(\vdots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) |
15 | 2.0008 | 4.0007 | 3.0008 | 5.0006 | 0.366883 |
16 | 2.0007 | 4.0005 | 3.0006 | 5.0005 | 0.151818 |
17 | 2.0005 | 4.0004 | 3.0005 | 5.0003 | 0.062895 |
18 | 2.0004 | 4.0003 | 3.0004 | 5.0002 | 0.026075 |
19 | 2.0003 | 4.0002 | 3.0003 | 5.0001 | 0.010815 |