Experiments and Design of an Inference Fuzzy System

The aim of this paper is to propose a criterion to estimate the design, from experimental data, of a fuzzy inference system, when data are sparse. This lack of data is important and may improve the generalisation ability of fuzzy systems (0). Several methods have been proposed to obtain automatic fuzzy rules from sparse training data. In (0), the authors first construct fuzzy rules from collect data. Then, they use kernel regressions for generate training data. Another technique used when classical inference methods produce sparse fuzzy rules is a diffusion procedure based on interpolation to initialize incomplete rules (0), (0), (0). Our method has the advantage of occuring before initialization step and therefore avoiding unfired rules which make difficult to produce an accurate output. .