Improvement of HSOFPNN using evolutionary algorithm

Authors:
Ho-Sung Park;Sung-Kwun Oh;Tae-Chon Ahn
Affiliations:
School of Electrical Electronic and Information Engineering, Wonkwang University, Iksan, South Korea;Department of Electrical Engineering, The University of Suwon, South Korea;School of Electrical Electronic and Information Engineering, Wonkwang University, Iksan, South Korea
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 4
Cited 0

Predicting a chaotic time series using a fuzzy neural network

Information Sciences: an International Journal
The design of self-organizing polynomial neural networks

Information Sciences—Informatics and Computer Science: An International Journal
Hybrid fuzzy polynomial neural networks

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Multi-layer hybrid fuzzy polynomial neural networks: a design in the framework of computational intelligence

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents genetically optimized Hybrid Self-Organizing Fuzzy Polynomial Neural Networks (gHSOFPNN). The architecture of the resulting gHSOFPNN results from a synergistic usage of the hybrid system generated by combining fuzzy polynomial neurons (FPNs)-based Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) with polynomial neurons (PNs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The augmented gHSOFPNN results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HSOFPNN. The GA-based design procedure being applied at each layer of gHSOFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HSOFPNN. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.