Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

Kang Li, Jian Xun Peng, E.W. Bai

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)
1 Downloads (Pure)

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 languageEnglish
Pages (from-to)630-643
Number of pages14
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume56
Issue number3
DOIs
Publication statusPublished - 2009

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems'. Together they form a unique fingerprint.

Cite this