Self-organizing hybrid neurofuzzy networks

Authors:
Sung-Kwun Oh;Su-Chong Joo;Chang-Won Jeong;Hyun-Ki Kim
Affiliations:
School of Electrical, Electronic and Information Engineering, Wonkwang University, South Korea;School of Electrical, Electronic and Information Engineering, Wonkwang University, South Korea;School of Electrical, Electronic and Information Engineering, Wonkwang University, South Korea;Department of Electrical Engineering, University of Suwon, South Korea
Venue:
ICCS'03 Proceedings of the 2003 international conference on Computational science
Year:
2003

Citing 6
Cited 0

A simply identified Sugeno-type fuzzy model via double clustering

Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems

Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
The design of self-organizing polynomial neural networks

Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling

IEEE Transactions on Fuzzy Systems
A new approach to fuzzy-neural system modeling

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce a concept of self-organizing Hybrid Neurofuzzy Networks(HNFN), a hybrid modeling architecture combining neurofuzzy (NF) and polynomial neural networks(PNN). The development of the Self-organizing HNFN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the Self-organizing HNFN results from a synergistic usage of NF and PNN. NF contribute to the formation of the premise part of the rule-based structure of the Self-organizing HNFN. The consequence part of the Self-organizing HNFN is designed using Self-organizing PNN. We also distinguish between two types of the Self-organizing HNFN architecture showing how this taxonomy depends on connection points. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process and to get output performance with superb predictive ability. The performance of the Self-organizing HNFN is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy models.