Evolution-fuzzy rule based system with parameterized consequences - Czekalski, Piotr (Zielona Góra: Uniwersytet Zielonogórski)
While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm.
Keywords: evolutionary strategy; fuzzy inference system; off-line learning; hybrid system