Epoch determination for neural network by self-organized map (SOM)
Artificial neural networks have a wide application in many areas of science and engineering and, particularly, in geotechnical problems with some degree of success due to the fact that the mechanical behavior of rocks are not salient. They are highly nonlinear, quite complex and complicated. While applying neural network in such complicated problems, epoch determination is based on hit-and-trail basis mainly. In this paper, the effect of different number of epochs is shown on the network and a method is proposed to determine the optimum number of epoch with the help of self-organized map (SOM) to avoid overtraining of the network. Data distribution is also done with the help of SOM and a statistical analysis is made to show consistency between training and testing dataset for ensuring the optimal model performance.
self-organizing map artificial neural network supervised learning unsupervised learning kohonen network