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Two-Stage Mixed Discrete-Continuous Identification of Radial Basis Function (RBF) Neural Models for Nonlinear Systems.
- Source :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers; Mar2009, Vol. 56 Issue 3, p630-643, 14p, 1 Black and White Photograph, 1 Chart, 1 Graph
- Publication Year :
- 2009
-
Abstract
- The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15498328
- Volume :
- 56
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers
- Publication Type :
- Periodical
- Accession number :
- 37014893
- Full Text :
- https://doi.org/10.1109/TCSI.2008.2002545