Optimal fuzzy modeling based on minimum cluster volume

Authors:
Can Yang;Jun Meng
Affiliations:
College of Electrical Engineering, Zhejiang University, Hangzhou, P. R. China;College of Electrical Engineering, Zhejiang University, Hangzhou, P. R. China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

Citing 7
Cited 1

A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy Modeling for Control

Fuzzy Modeling for Control
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions

SIAM Journal on Optimization
Clustering algorithms based on volume criteria

IEEE Transactions on Fuzzy Systems

MS based nonlinear methods for gastric cancer early detection

LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new fuzzy modeling method, which involves the Minimum Cluster Volume clustering algorithm. The cluster centers founded are naturally considered to be the centers of Gaussian membership functions. Covariance matrix obtained from the result of cluster method is made use to estimate the parameters σ for Gaussian membership functions. A direct result of this method are compared in our simulations with published methods, which indicate that our method is powerful so that it solves the multi-dimension problems more accurately even with less complexity of our fuzzy model structure.