NEURAL NETWORKS AS NONLINEAR STRUCTURE ACTIVITY RELATIONSHIP ANALYZERS - USEFUL FUNCTIONS OF THE PARTIAL DERIVATIVE METHOD IN MULTILAYER NEURAL NETWORKS

The operation of the perceptron-type neural network can be regarded as a function which transforms an input vector to another (output) vector. We have presented the analytical formula for the partial derivative of this function with respect to the elements of the input vector. Using numerical data, we have examined the accuracy, independency of the elements of the input vector, and recognition ability of a function in mixed functions. It was shown that the partial differential coefficients of output strength with respect to input parameters were useful to analyze the relationship between inputs and outputs in the neural network and characteristics of each data in the set of data.