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Table 1 Comparison of results

From: Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol

Training method Number of layers Number of neurons on each layer Mean squared error
Levenberg–Marquardt 2 15 0.15
Levenberg–Marquardt 3 15 0.51
Levenberg–Marquardt 4 15 0.157
Levenberg–Marquardt 2 20 1.7
Levenberg–Marquardt 3 20 0.36
Levenberg–Marquardt 4 20 0.08
Levenberg–Marquardt 2 25 0.79
Levenberg–Marquardt 3 25 0.04
Levenberg–Marquardt 4 25 184.5
Quasi-Newton 2 15 244.23
Quasi-Newton 3 15 781.88
Quasi-Newton 4 15 482.86
Quasi-Newton 2 20 351.43
Quasi-Newton 3 20 499.8
Quasi-Newton 4 20 217.64
Quasi-Newton 2 25 431.66
Quasi-Newton 3 25 172.11
Quasi-Newton 4 25 898.75
Scaled Conjugate Gradient 2 15 244.23
Scaled Conjugate Gradient 3 15 781.88
Scaled Conjugate Gradient 4 15 482.86
Scaled Conjugate Gradient 2 20 351.43
Scaled Conjugate Gradient 3 20 499.8
Scaled Conjugate Gradient 4 20 217.64
Scaled Conjugate Gradient 2 25 431.66
Scaled Conjugate Gradient 3 25 172.11
Scaled Conjugate Gradient 4 25 898.75
Fletcher–Powell 2 15 244.23
Fletcher–Powell 3 15 781.88
Fletcher–Powell 4 15 482.86
Fletcher–Powell 2 20 351.43
Fletcher–Powell 3 20 499.8
Fletcher–Powell 4 20 217.64
Fletcher–Powell 2 25 431.66
Fletcher–Powell 3 25 172.11
Fletcher–Powell 4 25 898.75
Polak–Ribiere 2 15 244.23
Polak–Ribiere 3 15 781.88
Polak–Ribiere 4 15 482.86
Polak–Ribiere 2 20 351.43
Polak–Ribiere 3 20 499.8
Polak–Ribiere 4 20 217.64
Polak–Ribiere 2 25 431.66
Polak–Ribiere 3 25 172.11
Polak–Ribiere 4 25 898.75