A novel technique of designing application specific defuzzification st
rategies with neural learning is presented. The proposed neural archit
ecture considered as a universal defuzzification approximator is valid
ated by showing the convergence when approximating several existing de
fuzzification strategies. The method is successfully tested with fuzzy
controlled reverse driving of a model truck. The transparent structur
e of the universal defuzzification approximator allows to analyze the
generated customized defuzzification method using the existing theorie
s of defuzzification, The integration of universal defuzzification app
roximator instead of traditional methods in Mamdani-type fuzzy control
lers can also be considered as an addition of trainable nonlinear nois
e to the output of the fuzzy rule inference before calculating the def
uzzified crisp output, Therefore, nonlinear noise trained specifically
for a given application shows a grade of confidence on the rule base,
providing an additional opportunity to measure the quality of the fuz
zy rule base, The possibility of modeling a Mamdani-type fuzzy control
ler as a feed-forward neural network with the ability of gradient desc
ent training of the universal defuzzification approximator and anteced
ent membership functions fulfill the requirement known from multilayer
preceptrons in finding solutions to nonlinear separable problems.