A Dynamic Clustering Algorithm Based on PSO and Its Application in Fuzzy Identification

Authors:
Dingxue Zhang;Xinzhi Liu;Zhihong Guan
Affiliations:
Huazhong University of Science & Technology, China;Huazhong University of Science & Technology, China;Huazhong University of Science & Technology, China
Venue:
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
Year:
2006

Citing 0
Cited 5

ARIMA Model Estimated by Particle Swarm Optimization Algorithm for Consumer Price Index Forecasting

AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
A quantum-inspired genetic algorithm for k-means clustering

Expert Systems with Applications: An International Journal
Modified fuzzy c-means for ordinal valued attributes with particle swarm for optimization

Fuzzy Sets and Systems
Optimal parameters estimation and input subset for grey model based on chaotic particle swarm optimization algorithm

Expert Systems with Applications: An International Journal
Integration of particle swarm optimization and genetic algorithm for dynamic clustering

Information Sciences: an International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

A dynamic clustering algorithm based on Particle Swarm Optimization (PSO) algorithm is proposed, in which a novel coding and operation on the basis of standard PSO is introduced and DB Index rule is used to determine the validity of clustering. The simulation results illustrate its veracity and efficiency. In the first place, the proper fuzzy rule number and exact premise parameters can be obtained by using the dynamic clustering algorithm to identify fuzzy models, and result parameters by the least squared method (LSM). The effectiveness and practicability is demonstrated by the simulation results of the Box-Jenkins gas furnace data comparing with other methods.