Nonlinear Systems Identification Using Additive Dynamic Neural Networks--Two On Line Approaches.
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/6047
Tipus de documentArticle
Data publicació2000-02
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
Abstract
This 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.
CitacióGriñó, R.; Cembrano, G.; Torras, C. "Nonlinear Systems Identification Using Additive Dynamic Neural Networks--Two On Line Approaches". IEEE Transactions on Circuits and Systems I-Regular Papers, 2000, Vol. 47, No. 2, p. 150-165.
ISSN1057-7122
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
IEEE_Nonlinear_system_identification.pdf | 379,1Kb | Visualitza/Obre |