Evolutionary neural fuzzy systems for data filtering

A new class of neural fuzzy filters for removing noise from 2-D measurement data is presented. The proposed approach combines the advantages of fuzzy and neural paradigms. The network structure is, in fact, specifically designed to exploit the effectiveness of fuzzy reasoning in removing noise without destroying the useful information embedded in the input data. Easy design of new filters is thus obtained because the neuro-fuzzy approach is capable of automatic acquisition of knowledge for a given network structure. The learning method, based on the genetic algorithms, performs an effective training of the network yielding satisfactory results after a few generations. Experimental results show that the proposed approach is very effective also in presence of data highly corrupted by noise. The neural fuzzy system is able to largely outperform other methods in the literature including state-of-the-art techniques.