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The 13 C NMR chemical shift sum ( CSS ) of alkanes was estimated with multi-linear regression (MLR) and multi-layer feed-forward artificial neural networks (ANN), using as structural descriptors the number of paths of length 1, 2, 3, and 4. The CSS prediction ability of both the MLR and ANN models was tested by the “leave-20%-out” (L20%O) cross-validation method. Four activation functions were tested in the neural model: the hyperbolic tangent, a bell-shaped function, a linear function and the symmetric logarithmoid function. The linear and symmetric logarithmoid functions were used only for the output layer. All combinations of activation functions give close results both in calibration and cross-validation, with somewhat lower performances for the networks with a bell-shaped output function. The best results were exhibited by the networks with the symmetric logarithmoid output function, followed by the networks with a linear output function. Because the results were very close, from a statistical point of view one could not definitively choose a particular combination of activation functions. The neural model provides better calibration and cross-validation results than the MLR model.

The 13C NMR chemical shift of sp2 carbon atoms in acyclic alkenes was estimated with multilayer feedforward artificial neural networks (ANNs) and multilinear regression (MLR), using as structural descriptors a vector made of 12 components encoding the environment of the resonating carbon atom. The neural network quantitative model provides better results than the MLR model calibrated with the same data. The predictive ability of both the ANN and MLR models was tested by the leave-20%-out (L20%O) cross-validation method, demonstrating the superior performance of the neural model. The number of neurons in the hidden layer was varied between 2 and 7, and three activation functions were tested in the neural model: the hyperbolic tangent or a bell-shaped function for the hidden layer and a linear or a hyperbolic tangent function for the output layer. All four combinations of activation functions give close results in the calibration of the ANN model, while for the prediction a linear output function performs ...

The 13C NMR chemical shift of sp2 carbon atoms in acyclic alkenes was estimated with multilayer feedforward artificial neural networks (ANNs) and multilinear regression (MLR), using as structural descriptors a vector made of 12 components encoding the environment of the resonating carbon atom. The neural network quantitative model provides better results than the MLR model calibrated with the same data. The predictive ability of both the ANN and MLR models was tested by the leave-20%-out (L20%O) cross-validation method, demonstrating the superior performance of the neural model. The number of neurons in the hidden layer was varied between 2 and 7, and three activation functions were tested in the neural model: the hyperbolic tangent or a bell-shaped function for the hidden layer and a linear or a hyperbolic tangent function for the output layer. All four combinations of activation functions give close results in the calibration of the ANN model, while for the prediction a linear output function performs ...