A fuzzy cognitive structure is examined, and the problem of numerical and symbolic processing in terms of neurocomputations and fuzzy sets is addressed. The referential neural network architecture previously developed is recalled, and aspects of the VLSI implementation are explored with emphasis on fault tolerance and testability. Preprocessing and postprocessing are used with a feedforward neural network in the hardware implementation, and development of the supporting software environment is also investigated. The result is a very general structure that can be used to address a wide variety of complex problems
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