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

Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction