Korbicz, Józef - red. ; Uciński, Dariusz - red.
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. ; The presented method consists of the following steps: obtaining an initial set of rules with parameterized consequences using the Michigan approach combined with an evolutionary strategy and a covering algorithm for the training data set; reducing the obtained rule base using a simple genetic algorithm; multi-phase tuning of the fuzzy inference system with parameterized consequences using the Pittsburgh approach and an evolutionary strategy. ; The paper presents experimental results using popular benchmark data sets regarding system identification and time series prediction, providing a reliable comparison to other learning methods, particularly those based on neuro-fuzzy, clustering and ?-insensitive methods. An examplary fuzzy inference system with parameterized consequences using the Reichenbach implication and the minimum t-norm was implemented to obtain numerical results.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 16, number 3 (2006) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Sep 7, 2021
Aug 26, 2020
|Evolution-fuzzy rule based system with parameterized consequences||Sep 7, 2021|
Simiński, Krzysztof Korbicz, Józef - red. Uciński, Dariusz - red.
Dalton, Tracy Klotzek, Petra Frank, Paul M. Korbicz, Józef - red. Patton, Ronald J. - red.
Busłowicz, Mikołaj Ruszewski, Andrzej Korbicz, Józef - red. Uciński, Dariusz - red.
Zhai, Guisheng Matsumoto, Yuuki Chen, Xinkai Imae, Joe Kobayashi, Tomoaki Beliczyński, Bartłomiej - red.