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Established methods of Boolean minimization have previously unseen potential as an efficient and unrestricted means of fuzzy structure discovery, becoming particularly useful within a design methodology for the automatic development of fuzzy models. Traditionally used in digital systems design, logic minimization tools allow us to exploit the fundamental links between binary (two-valued) and fuzzy (multivalued) logic. In this paper, we show how logic optimization plays an integral role in a two-phase fuzzy model design process. Adaptive logic processing is realized as the discovered Boolean structures are augmented with fuzzy granules and then refined by adjusting connections of fuzzy neurons, helping to further capture the numeric details of the target systems behavior. Accurate and highly interpretable fuzzy models are the result of the entire development process.