Paper Info
Title
A transformed input-domain approach to fuzzy modeling
Abstract
This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm
Year
DOI
Venue
1998
10.1109/91.728458
IEEE T. Fuzzy Systems
Keywords
Field
DocType
linear model,transformed input-domain approach,input space partitioning,sample data component correlation,input data,conventional fuzzy modeling algorithm,input-domain approach,correlation theory,ineffective partition,new fuzzy modeling algorithm,sample data,fuzzy modeling,fuzzy systems,pca,input space,computer simulation,fuzzy modeling algorithm,principal component analysis,fuzzy model,modelling,indexing terms,helium,neural networks,principal component,covariance matrix
Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Fuzzy control system,Fuzzy number,Fuzzy associative matrix,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
6
4
1063-6706
Citations 
PageRank 
References 
71
3.60
11
Authors
4
Name
Order
Citations
PageRank
Euntai Kim11472109.36
Minkee Park237525.10
Seung-Woo Kim323115.16
Mignon Park475970.43