The objective of the present work is to develop one technique for the modeling of systems, capable of if adapting to a wide classroom of problems. As innovative aspect this the form as the modeling of the signal, made is guided according to analysis of the spaces of the entrance signals and exit, of these you analyze are made partitions iteratively in such spaces until reaching the desired error of modeling. The technique proposal was developed here using neural nets RBF and Neuro-fuzzy models, helping itself with the objective to generate a estimate next to the ideal, this aid lends is made by the lineal combination of the autovetores and autovalores, form such, to generate new autovetores and autovalores next to the ideals. An extra objective, associated to the process of identification of systems, is the incorporation of easinesses of identification in the relations enter-exit by means of techniques of spectral decomposition. Of this form, on the other hand it is intended to reduce the time of training and analysis for the identification, being eliminated tests a priori unnecessary judgeships. On the other hand, this technique would sinalaria ways for more viable solutions to the process.