Paper Info
Title
Evolving Fuzzy-Model-based Design of Experiments with Supervised Hierarchical Clustering
Abstract
This paper presents a new approach to design of experiments (DoE), based on an evolving fuzzy model structure and a supervised, hierarchical clustering algorithm. Design of experiments is the field that deals with the problem how to design the most optimal and economic experimentation. The goal is to identify a highly non-linear and possibly high-dimensional system together with the minimal experimental effort required. The theory is well developed for linear and polynomial models; however, they are often not suitable for general use. For this reason a fuzzy model in the form of Takagi-Sugeno is used, because it has the properties of a universal approximator. The method works iteratively by sampling the system in the input domain and evolving the fuzzy model. The method is demonstrated with a simulation, which shows the potential of the proposed approach.
Year
DOI
Venue
2015
10.1109/TFUZZ.2014.2329711
Fuzzy Systems, IEEE Transactions  
Keywords
Field
DocType
clustering algorithms,data models,mathematical model,vectors,algorithm design and analysis
Hierarchical clustering,Data mining,Canopy clustering algorithm,Data modeling,Nonlinear system,Algorithm design,Polynomial,Artificial intelligence,Cluster analysis,Machine learning,Mathematics,Design of experiments
Journal
Volume
Issue
ISSN
PP
99
1063-6706
Citations 
PageRank 
References 
9
0.50
9
Authors
1
Name
Order
Citations
PageRank
Igor Skrjanc135452.47