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.
Original language | English |
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Pages (from-to) | 630-643 |
Number of pages | 14 |
Journal | IEEE Transactions on Circuits and Systems I: Regular Papers |
Volume | 56 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2009 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering