A special multilayer perceptron architecture known as FuNe I is succes
sfully used for generating fuzzy systems for a number of real world ap
plications. The FuNe I trained with supervised learning can be used to
extract fuzzy rules from a given representative input/output data set
. Furthermore, optimization of the knowledge base is possible includin
g the tuning of membership functions. The new method employed to ident
ify the rule relevant nodes before the rules are extracted makes FuNe
I suitable for applications with large number of inputs. Some of the r
eal world applications in areas of state identification and image clas
sification show encouraging results in a shorter development time. Exp
ert knowledge is not compulsory but can be included in the automatical
ly extracted knowledge base. The generated fuzzy system can be impleme
nted in hardware very easily. A flexible prototype board is developed
with a FPGA chip in order to run applications with up to 128 inputs an
d 4 outputs in realtime (1.25 million rules per second).