  The choice of architecture of artificial neuron network (ANN) is still a
challenging task that users face every time. It greatly affects the accuracy of
the built network. In fact there is no optimal method that is applicable to
various implementations at the same time. In this paper we propose a method to
construct ANN based on clustering, that resolves the problems of random and ad
hoc approaches for multilayer ANN architecture. Our method can be applied to
regression problems. Experimental results obtained with different datasets,
reveals the efficiency of our method.
