  The normalized radial basis function neural network emerges in the
statistical modeling of natural laws that relate components of multivariate
data. The modeling is based on the kernel estimator of the joint probability
density function pertaining to given data. From this function a governing law
is extracted by the conditional average estimator. The corresponding
nonparametric regression represents a normalized radial basis function neural
network and can be related with the multi-layer perceptron equation. In this
article an exact equivalence of both paradigms is demonstrated for a
one-dimensional case with symmetric triangular basis functions. The
transformation provides for a simple interpretation of perceptron parameters in
terms of statistical samples of multivariate data.
