Some Theorems for Feed Forward Neural Networks

This paper introduces a new method which employs the concept of “Orientation Vectors” to train a feed forward neural network. It is shown that this method is suitable for problems where large dimensions are involved and the clusters are characteristically sparse. For such cases, the new method is not NP hard as the problem size increases. We ‘derive’ the present technique by starting from Kolmogrov’s method and then relax some of the stringent conditions. It is shown that for most classification problems three layers are sufficient and the number of processing elements in the first layer depends on the number of clusters in the feature space. This paper explicitly demonstrates that for large dimension space as the number of clusters increase from N to N+dN the number of processing elements in the first layer only increases by d(logN), and as the number of classes increase, the processing elements increase only proportionately, thus demonstrating that the method is not NP hard with increase in problem size. Many examples have been explicitly solved and it has been demonstrated through them that the method of Orientation Vectors requires much less computational effort than Radial Basis Function methods and other techniques wherein distance computations are required, in fact the present