Evaluation of correlation methods applying neural networks

This paper deals with a new perception of classic correlation methods on the basis of neural nets. We present and examine neural nets that evaluate the similarity between two arbitrary vectors in a process of measurement or pattern recognition. Thus, classifying patterns by means of feature vectors is feasible just as by correlation methods. Furthermore, we show that the difference correlation procedure and the squared-distance correlation procedure can be presented directly as special cases of the neural methods. Using an example of a typical recognition problem and Gaussian-distributed measuring errors, computer simulations have yielded that neural and correlation procedures are almost identical in behaviour regarding the error rates. Consequently, the neural procedures presented can be understood as a generalisation of correlation procedures.