Research Article
Comparison of Artificial Neural Network Architecture in Solving Ordinary Differential Equations
Table 2
Comparison of the results (Example
1).
| Input data | Analytical | Euler | Runge-Kutta | āā (Six nodes) |
| 0 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.0500 | 0.9536 | 0.9500 | 0.9536 | 0.9677 | 0.1000 | 0.9137 | 0.9072 | 0.9138 | 0.9159 | 0.1500 | 0.8798 | 0.8707 | 0.8799 | 0.8815 | 0.2000 | 0.8514 | 0.8401 | 0.8515 | 0.8531 | 0.2500 | 0.8283 | 0.8150 | 0.8283 | 0.8264 | 0.3000 | 0.8104 | 0.7953 | 0.8105 | 0.8114 | 0.3500 | 0.7978 | 0.7810 | 0.7979 | 0.7953 | 0.4000 | 0.7905 | 0.7721 | 0.7907 | 0.7894 | 0.4500 | 0.7889 | 0.7689 | 0.7890 | 0.7845 | 0.5000 | 0.7931 | 0.7717 | 0.7932 | 0.7957 | 0.5500 | 0.8033 | 0.7805 | 0.8035 | 0.8041 | 0.6000 | 0.8200 | 0.7958 | 0.8201 | 0.8204 | 0.6500 | 0.8431 | 0.8178 | 0.8433 | 0.8399 | 0.7000 | 0.8731 | 0.8467 | 0.8733 | 0.8711 | 0.7500 | 0.9101 | 0.8826 | 0.9102 | 0.9151 | 0.8000 | 0.9541 | 0.9258 | 0.9542 | 0.9555 | 0.8500 | 1.0053 | 0.9763 | 1.0054 | 0.9948 | 0.9000 | 1.0637 | 1.0342 | 1.0638 | 1.0662 | 0.9500 | 1.1293 | 1.0995 | 1.1294 | 1.1306 | 1.000 | 1.2022 | 1.1721 | 1.2022 | 1.2058 |
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