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Prediction of 13C NMR Chemical Shifts by Neural Networks in a Series of Monosubstituted Benzenes

Š. Sklenák, V. Kvasnička, and J. Pospíchal

Chemical Faculty, Technical University, CZ-637 00 Brno

 

Abstract: Feed-forward back-propagation neural networks with one output neuron were used. This approach was compared with a neural network with four output neurons giving chemical shifts of ipso, ortho, meta, and para positions in a series of monosubstituted benzenes. One-output neural networks, each one giving shift for one position of carbon, seem to give better results than the four-output neural network. The work shows that finely tuned simple neural network can work better than the sophisticated neural methods.

Full paper in Portable Document Format: 483a135.pdf

 

Chemical Papers 48 (3) 135–140 (1994)

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