Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30035
Share/Export:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Title

Nonlinear system identification using additive dynamic neural networks - two on-line approaches

AuthorsGriñó, Robert; Cembrano, Gabriela CSIC ORCID ; Torras, Carme CSIC ORCID
KeywordsAdditive dynamic neural networks
Identification
Invariant imbedding theory
Sensitivity analysis
Variational calculus
Control theory
Issue Date2000
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 47(2): 150-165, 2000.
AbstractThis paper proposes a class of additive dynamic connectionist (ADC) models for identification of unknown dynamic systems. These models work in continuous time and are linear in their parameters. Also, for this kind of model two on-line learning or parameter adaptation algorithms are developed: one based on gradient techniques and sensitivity analysis of the model output trajectories versus the model parameters and the other based on variational calculus, that lead to an off-line solution and an invariant imbedding technique that converts the off-line solution to an on-line one. These learning methods are developed using matrix calculus techniques in order to implement them in an automatic manner with the help of a symbolic manipulation package. The good behavior of the class of identification models and the two learning methods is tested on two simulated plants and a data set from a real plant and compared, in this case, with a feedforward static (FFS) identifier.
Publisher version (URL)http://dx.doi.org/10.1109/81.828569
URIhttp://hdl.handle.net/10261/30035
DOI10.1109/81.828569
ISSN1057-7122
Appears in Collections:(IRII) Artículos

Files in This Item:
File Description SizeFormat
doc1.pdf379,12 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work

SCOPUSTM   
Citations

33
checked on May 21, 2022

WEB OF SCIENCETM
Citations

30
checked on May 15, 2022

Page view(s)

326
checked on May 21, 2022

Download(s)

348
checked on May 21, 2022

Google ScholarTM

Check

Altmetric

Dimensions


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.