Modelling metabolic energy by neural networks

The apparent metabolic energy (EMA) of barley is modelled as a function of 12 easily obtainable analytical parameters by applying neural networks with the error back-propagation learning strategy. Kohonen maps and Wardʹs clustering technique have been used to define the objects for the training and test sets. The architecture of the neural network and the relevant parameters of error back-propagation learning have been optimised providing a RMS of 1.081 and a correlation coefficient (predicted versus found values) of 0.82. Contour maps of all variables including the output EMA value have been obtained by applying the counter-propagation learning strategy in a two-layer neural network. The responses yielded by the networks show that this method is capable of establishing a quantitative relationship between EMA and the original variables.