Applied Fuzzy Systems

Data processing not only in physics and engineering, but also in medicine, biology, sociology, economics, sport, art, and military affairs, amounts to the different statements of identification problems. Fuzzy logic is mistakenly perceived by many specialists in mathematical simulation as a mean of only approximate decisions making in medicine, economics, art, sport and other different from physics and engineering humanitarian domains, where the high level of accuracy is not required. Therefore, one of the main goals of the authors is to show that it is possible to reach the accuracy of modeling, which does not yield to strict quantitative correlations, by tuning fuzzy knowledge bases. Only objects with discrete outputs for the direct inference and discrete inputs for the inverse inference were considered in the previous chapters. Such a problem corresponds to the problem of automatic classification arising in particular from medical and technical diagnosis. The main idea which the authors strive to render is that while tuning the fuzzy knowledge base it is possible to identify nonlinear dependencies with the necessary precision.