Subtractive fuzzy C-means clustering approach with applications to fuzzy predictive control

Authors:
Ji-Hang Zhu;Hong-Guang Li
Affiliations:
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China;College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
Venue:
WSEAS Transactions on Systems and Control
Year:
2011

Citing 3
Cited 0

Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Some new indexes of cluster validity

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Development of a systematic methodology of fuzzy logic modeling

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

To identify T-S models, this paper presents a so-called "subtractive fuzzy C-means clustering" approach, in which the results of subtractive clustering are applied to initialize clustering centers and the number of rules in order to perform adaptive clustering. This method not only regulates the division of fuzzy inference system input and output space and determines the relative member function parameters, but also overcomes the impacts of initial values on clustering performance. Additionally, the orthogonal least square algorithm is employed to identify the parameters of consequents and linearize the systems over every sample time, ultimately resulting in the entire T-S fuzzy models. With this approach available, a fuzzy model predictive control system is established, along with corresponding control algorithms derived, as well as control system simulations carried out which explicitly demonstrate the effectiveness of the proposed method.