Simultaneous Optimization of ANFIS-Based Fuzzy Model Driven to Data Granulation and Parallel Genetic Algorithms

Authors:
Jeoung-Nae Choi;Sung-Kwun Oh;Ki-Sung Seo
Affiliations:
Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea;Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea;Department of Electronics Engineering, Seokyeong University, South Korea
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

Citing 3
Cited 0

Handbook of software reliability engineering

Handbook of software reliability engineering
Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems

Fuzzy Sets and Systems
Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper concerns the simultaneous optimization for structure and parameters of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. HFCGA is used to optimize structure and parameters of ANFIS-based fuzzy model simultaneously. The granulation is realized with the aid of the C-means clustering. Through the simultaneous optimization mechanism to be explored, we can find the overall optimal values related to structure as well as parameter identification of ANFIS-based fuzzy model via HFCGA, C-Means clustering and standard least square method. A comparative analysis demon-strates that the proposed algorithm is superior to the conventional methods.