A novel self-organizing fuzzy polynomial neural networks with evolutionary FPNs: design and analysis

Authors:
Ho-Sung Park;Sung-Kwun Oh;Tae-Chon Ahn
Affiliations:
School of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;School of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 6
Cited 1

Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic

Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
The design of self-organizing polynomial neural networks

Information Sciences—Informatics and Computer Science: An International Journal
Hybrid identification in fuzzy-neural networks

Fuzzy Sets and Systems - Theme: Learning and modeling
Multi-layer hybrid fuzzy polynomial neural networks: a design in the framework of computational intelligence

Neurocomputing
Comparison of adaptive methods for function estimation from samples

IEEE Transactions on Neural Networks

Evolutionary design of gdSOFPNN for modeling and prediction of NOx emission process

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, we introduce a new category of neurofuzzy networks – Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) and develop a comprehensive design methodology involving mechanisms of genetic algorithms and information granulation. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of SOFPNN leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network.