EFFECTS OF LEARNING PARAMETERS AND DATA PRESENTATION ON THE PERFORMANCE OF BACKPROPAGATION NETWORKS FOR MILK YIELD PREDICTION
The effect of four different learning parameters as well as the method of data presentation was investigated 
with regard to the performance of backpropagation neural networks in predicting milk yield. The parameters examined were: (1) the learning rates of the hidden and output layer, (2) momentum, (3) epoch size, and (4) a binary/bipolar data presentation. The modified values of each parameter included the extremes and commonly used levels found in the literature. Modifications were made one at a time, and network training was repeated with different sets of initial weights. In addition, combinations of bipolar data presentation with different epoch sizes were also formed to study their combined effect. Although all the networks learned their training data quite well, and quite similarly, some differences were observed in the results of test data. The most notable effects were detected when the epoch assumed its extreme sizes, and when a bipolar data representation was used. The lowest network root mean square errors corresponded equally to the smallest epoch size, a bipolar data presentation, and their combination. Increasing the value of the learning rate in the hidden layer tended to improve network performance. Conversely, large learning rates in the output layer tended to increase the network error. With regard to different values of the momentum, the results were quite similar. The results of 
repetitions, for all parameters, revealed that the initial weights influenced network performance. Results obtained in this study suggest that the users of artificial neural networks should pay attention to the values of different learning parameters and the method of data presentation. They should also carry out several repetitions, using different initial weight values, in order to optimize the network results.