GAs for fuzzy modeling of noise pollution

A growing problem in town areas is noise pollution due to the increasing number of vehicles that daily cross cities. A classical approach to model this kind of system is based on numerical regression, but its performance is not satisfactory due to the nonlinearity of the considered model. A suitable approach can be therefore to determine a fuzzy model of the system. There has been a considerable number of studies on fuzzy identification, where fuzzy implications are used to express rules, in this paper the Tagaki-Sugeno approach has been adopted applying a genetic algorithm during the optimization phase. The obtained models are compared with traditional ones showing the suitability of the proposed method.