TY - GEN
A1 - Czekalski, Piotr
A2 - Korbicz, Józef - red.
A2 - Uciński, Dariusz - red.
PB - Zielona Góra: Uniwersytet Zielonogórski
N2 - 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.
N2 - 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.
N2 - 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.
L1 - http://zbc.uz.zgora.pl/Content/57546/AMCS_2006_16_3_8.pdf
L2 - http://zbc.uz.zgora.pl/Content/57546
KW - evolutionary strategy
KW - fuzzy inference system
KW - off-line learning
KW - hybrid system
T1 - Evolution-fuzzy rule based system with parameterized consequences
UR - http://zbc.uz.zgora.pl/dlibra/docmetadata?id=57546
ER -