ASIC implementation of the symmetric fuzzy processor and its application to adaptive systems

TL;DRAbstract

This dissertation presents a VLSI design of a symmetric fuzzy processor. The design features fuzzification, defuzzification and inference operations while allowing the implementation of a knowledge base via rules. By combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets, a novel structure for the fuzzification model is obtained. The structure accelerates the evaluation of the antecedent degree which is evaluated by a simple mathematical relation which calculates the resulting value using the end-points of matched fuzzy member functions. This feature enables the processor to avoid the requirement of storing all the sample values of the fuzzy membership function in memory as is the case with other approaches. In addition, the resulting design structure simplifies computations associated with centroid defuzzication in that certain simplifying assumptions eliminate the need for a divider circuit. By using a very high speed integrated circui

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