Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
IEEE Spectrum
A parameterized fuzzy processor and its applications
Fuzzy Sets and Systems
GEFRED: a generalized model of fuzzy relational databases
Information Sciences—Informatics and Computer Science: An International Journal
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The fuzzy controller architecture presented in this paper consists of a synchronous digital system which process fuzzy sets at two abstraction levels: fuzzy set level and domain element level. Its main features are the use of trapezia, encoded with four parameters, to represent the antecedent labels. The defuzzification is based in a novel algorithm which speeds up this process by avoiding the operation of division. The performance of the processor is not a constant but depends on the location of the centroid within the domain of the consequent, being better when the centroid is located at the centre of the domain, and worse when it becomes near to one of the ends. Consequently, it takes advantage from the fact that the centroid in an end is the least probable situation. Any case the processor gets significant improvements in speed and latency.