Combining logical-type neuro-fuzzy systems

Authors:
Marcin Korytkowski;Robert Nowicki;Leszek Rutkowski;Rafał Scherer
Affiliations:
Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland;Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland;Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland;Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

Citing 13
Cited 0

Adaptive fuzzy systems and control: design and stability analysis

Adaptive fuzzy systems and control: design and stability analysis
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy Modeling for Control

Fuzzy Modeling for Control
Fuzzy Control and Fuzzy Systems

Fuzzy Control and Fuzzy Systems
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Foundations of Neuro-Fuzzy Systems

Foundations of Neuro-Fuzzy Systems
An introduction to boosting and leveraging

Advanced lectures on machine learning
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science)

Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science)
A brief introduction to boosting

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Fuzzy Classifier Design

Fuzzy Classifier Design
Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems

IEEE Transactions on Fuzzy Systems
Flexible neuro-fuzzy systems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Boosting is one of the most popular methods of multiple classification. In the paper we propose a method for merging several logical-type neuro-fuzzy systems that come from boosting ensemble into one neuro-fuzzy system. Thanks to this we can use all rule-bases as one system.