A method for transformation of fuzzy logic (FS) into a neural network (NN), in which, in order to form a defuzzified output value (y2) from normalized single-element functions (F1 . . . Fm), the single-element functions (F1 . . . Fm) are each assigned a singleton position (A1 . . . Am) and at least one singleton weighting factor (R1 . . . Rn), those singleton weighting factors (R1 . . . Rn) which are assigned to the same single-element function (F1 . . . Fm) are additively linked, and the singleton weighting factors (R1 . . . Rn) and the additively linked singleton weighting factors (R1 . . . Rn) are weighted via the corresponding singleton positions (A1 . . . Am) and are additively linked in order to form the defuzzified output value (y2). One advantage of the method according to the invention is that the singleton positions (A1 . . . Am) in the neural network (NN) can be varied, in order to optimize this network, such that their number before and after the optimization process remains constant and thus, in any case, subsequent reverse transformation of the neural network (NN) can be carried out to optimize fuzzy logic (FS). This advantageously allows the use of, in particular, standardized fuzzy system software to describe the optimized fuzzy logic (FS).
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