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Table 2 Different ANN networks architectures for traffic volume prediction

From: ANN based short-term traffic flow forecasting in undivided two lane highway

Model Algorithm Hidden layer hidden neurons Transfer Function Epochs Learning Step size/Mo Training Cross validation Testing
Min MSE (*104) Final MSE (*104) Min MSE (*104) Final MSE (*104) MSE NMSE (*104) MAE R (%)
M1 MLP 1 4 Tanh 100 LM 5.9 5.9 12.5 10.37 4.66 47.99 1.93 98.8
M2 MLP 1 4 Tanh 200 LM 5.3 5.3 15 7.13 6.00 61.81 2.14 98.4
M3 MLP 1 4 Tanh 300 LM 4 4 21.1 20.94 3.25 33.49 1.26 98.7
M4 MLP 1 5 Tanh 100 LM 3.6 3.8 20.24 8.371 4.79 49.34 1.84 99.2
M5 MLP 1 5 Tanh 200 LM 2.8 2.8 16.05 16.866 19.8 204.4 3.73 93.7
M6 MLP 1 5 Tanh 100 MO 1/0.7 29.9 29.9 23.19 2.506 4.12 42.43 1.64 98.3
M7 MLP 1 5 Tanh 200 MO 1/0.7 27.3 27.3 25.8 3.3 7.10 73.12 2.06 96.6
M8 MLP 1 3 SigmoidAxon 100 LM 2.6 2.6 3.09 0.832 7.980 82.1 2.32 97.9
M9 MLP 1 3 SigmoidAxon 200 LM 1.6 1.8 2.63 0.263 17.66 181.8 3.13 92.7
M10 MLP 1 5 SigmoidAxon 100 LM 0.81 0.8141 3.73 1.883 15.012 154.55 3.44 95.6
M11 MLP 1 5 SigmoidAxon 200 LM 0.03 0.0358 4.92 13.479 151.9 1564.4 11.18 25.5
M12 MLP 1 7 SigmoidAxon 150 LM 0.36 0.366 4.7 2.89 0.816 8.4 0.77 99.8