Title
Model-based design of experiments based on local model networks for nonlinear processes with low noise levels
Abstract
Most common methods for experiment design are classical, geometric designs and optimal designs. Both categories of methods don't incorporate specific information about the process behavior into the design of experiments. In the case of optimal design often the underlying model structure is chosen as low order polynomial which is very restricted in its flexibility and causes problems, if used for higher-dimensional problems. Furthermore, the focus of these approaches lies on the minimization of the variance error. However, in many applications the process noise is negligible in comparison to the highly nonlinear behavior which usually causes a large bias error. Therefore, this paper presents the new algorithm HilomotDoE which is an active learning algorithm that aims to minimize the bias error of the model. This is achieved by an iterative refinement of a local model network and simultaneously the addition of a certain amount of measurement points. Demonstration examples and theoretical comparisons with the common D-optimal design show the usefulness of HilomotDoE for the mentioned problem class.
Year
DOI
Venue
2011
10.1109/ACC.2011.5990833
American Control Conference
Keywords
Field
DocType
design of experiments,learning (artificial intelligence),minimisation,polynomials,hilomotdoe,active learning algorithm,geometric design,higher-dimensional problem,local model network,low order polynomial,minimization,model-based design of experiment,nonlinear process,optimal design,process behavior,variance error,data models,data model,model based design,engines,algorithm design and analysis,computational modeling,computer model,covariance matrix,learning artificial intelligence
Iterative refinement,Data modeling,Mathematical optimization,Algorithm design,Polynomial,Computer science,Model-based design,Optimal design,Geometric design,Design of experiments
Conference
ISSN
ISBN
Citations 
0743-1619
978-1-4577-0080-4
2
PageRank 
References 
Authors
0.40
6
3
Name
Order
Citations
PageRank
Benjamin Hartmann1252.31
Ebert, T.230.75
Oliver Nelles39917.27