Abstract

A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism stores an initial knowledge base via approximate learning and utilizes this information for decision-making systems via fuzzy inferencing. This fuzzy computer architecture, a fuzzy hypercube, processes all the rules in one clock period in parallel. Fuzzy hypercubes can be applied to control of a class of complex and highly nonlinear systems which suffer from vagueness or uncertainty. >

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