Training of Neural Networks by Modified Taguchi Techniques

Modified Taguchi techniques of robust design optimization are used in an innovative method of training artificial neural networks — for example the network shown in the figure. As in other neural-network-training methods, the synaptic weights (strengths of connections between neurons) are adjusted iteratively in an effort to reduce a cost function, which is usually the sum of squared errors between the actual network outputs and the prescribed (correct) network outputs for training sets of inputs. However, this method offers advantages over older methods, as explained below.