Paper Info
Title
Experimental Design And Model Construction Algorithms For Radial Basis Function Networks
Abstract
New construction algorithms for radial basis function (RBF) network modelling are introduced based on the A-optimality and D-optimality experimental design criteria respectively. We utilize new cost functions, based on experimental design criteria, for model selection that simultaneously optimizes model approximation, parameter variance (A-optimality) or model robustness (D-optimality). The proposed approaches are based on the forward orthogonal least-squares (OLS) algorithm, such that the new A-optimality- and D-optimality-based cost functions are constructed on the basis of an orthogonalization process that gains computational advantages and hence maintains the inherent computational efficiency associated with the conventional forward OLS approach. The proposed approach enhances the very popular forward OLS-algorithm-based RBF model construction method since the resultant RBF models are constructed in a manner that the system dynamics approximation capability, model adequacy and robustness are optimized simultaneously. The numerical examples provided show significant improvement based on the D-optimality design criterion, demonstrating that there is significant room for improvement in modelling via the popular RBF neural network.
Year
DOI
Venue
2003
10.1080/00207720310001640223
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Keywords
Field
DocType
system dynamics,cost function,model selection,d optimal design,radial basis function,experimental design,radial basis function network
Radial basis function network,Mathematical optimization,Radial basis function,Hierarchical RBF,Model selection,Algorithm,Robustness (computer science),System dynamics,Construction method,Orthogonalization,Mathematics
Journal
Volume
Issue
ISSN
34
14-15
0020-7721
Citations 
PageRank 
References 
11
0.76
8
Authors
2
Name
Order
Citations
PageRank
X. Hong115711.12
C. J. Harris2323.34