Neural Networks in Designing Fuzzy Systems for Real World Applications

A special multilayer percep tron architecture known as FuNe I is suc cessfully used for generating fuzzy systems for a number of real world applications The FuNe I trained with supervised learn ing can be used to extract fuzzy rules from a given representative input output data set Furthermore optimization of the knowledge base is possible including the tuning of membership functions The new method employed to identify the rule rel evant nodes before the rules are extracted makes FuNe I suitable for applications with large number of inputs Some of the real world applications in ar eas of state identi cation and image clas si cation show encouraging results in a shorter development time Expert knowl edge is not compulsory but can be included in the automatically extracted knowledge base The generated fuzzy system can be implemented in hardware very easily A exible prototype board is developed with a FPGA chip in order to run applications with up to inputs and outputs in re altime million rules per second