Abstract Identification of an output error model using the prediction error method leads to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because in most cases both the corresponding objective function and the search space are nonconvex. The difficulty in solving the optimization problem depends mainly on the experimental conditions, more specifically on the spectra of the input/output data collected from the system. It is therefore possible to improve the convergence of the algorithms by properly choosing the data prefilters; in this paper we show how to perform this choice. We present the application of the proposed approach to case studies where the standard algorithms tend to fail to converge to the global minimum.

The article examines the effects of the change in the number of municipal councilors in Brazilian municipalities, in the 2004 elections, on political representation. The experimental method is used. It reveals that the effects are the increase of the disproportionality of the electoral system and the party concentration, but in levels that do not take away its fundamental characteristics, especially the maintenance of the multiparty system. It also examines the regional distribution of these effects.