Abstract
This paper presents a hardware application of a self—learning fuzzy logic controller on industrial gas furnace data. The fuzzy algorithm is based upon a unique probability theory approach to fuzzy identification and estimation. This method was chosen because of an inherent filtering operation making it highly immune to noise. In the past the majority of fuzzy inference processors have been designed as application-specific circuits or custom IC’s. Using semi—custom programmable IC’s resolves the problems of development costs, adaptability of designs and execution speed.
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