Sliding-window neural state estimation in a power plant heater Sliding--window neural state estimation in a power plant heater line (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• Sliding-window neural state estimation in a power plant heater Sliding--window neural state estimation in a power plant heater line (Articolo in rivista) (literal)

Anno

• 2001-01-01T00:00:00+01:00 (literal)

Alternative label

• Alessandri A. 1, Parisini T. 2, Zoppoli R. 3 (2001)
  Sliding-window neural state estimation in a power plant heater Sliding--window neural state estimation in a power plant heater line
  in International journal of adaptive control and signal processing (Print)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Alessandri A. 1, Parisini T. 2, Zoppoli R. 3 (literal)

Pagina inizio

• 815 (literal)

Pagina fine

• 836 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 15 (literal)

Rivista

• International journal of adaptive control and signal processing (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto

• Pubblicazione su rivista internazionale John Wiley & Sons (literal)

Note

• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• 1 CNR, 2 Uni Trieste, 3 Uni Genova (literal)

Titolo

• Sliding-window neural state estimation in a power plant heater Sliding--window neural state estimation in a power plant heater line (literal)

Abstract

• The state estimation problem for a section of a real power plant is addressed by means of a recently proposed sliding-window neural state estimator. The complexity and the nonlinearity of the considered application prevent us from successfully using standard techniques as Kalman filtering. The statistics of the distribution of the initial state and of noises are assumed to be unknown and the estimator is designed by minimizing a given generalized least-squares cost function. The following approximations are enforced: (i) the state estimator is a finite-memory one, (ii) the estimation functions are given fixed structures in which a certain number of parameters have to be optimized (multilayer feedforward neural networks are chosen from among various possible nonlinear approximators), (iii) the algorithms for optimizing the parameters (i.e., the network weights) rely on a stochastic approximation. Extensive simulation results on a complex model of a part of a real power plant are reported to compare the behaviour of the proposed estimator with the extended Kalman filter. (literal)

Prodotto di

• Institute of intelligent systems for automation (ISSIA) (Istituto)

Autore CNR

• ANGELO ALESSANDRI (Persona)

Insieme di parole chiave

• Parole chiave di "Sliding-window neural state estimation in a power plant heater Sliding--window neural state estimation in a power plant heater line" (Insieme di parole chiave)

Incoming links:

Prodotto

• Institute of intelligent systems for automation (ISSIA) (Istituto)

Autore CNR di

• ANGELO ALESSANDRI (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• International journal of adaptive control and signal processing (Rivista)

Insieme di parole chiave di

• Parole chiave di "Sliding-window neural state estimation in a power plant heater Sliding--window neural state estimation in a power plant heater line" (Insieme di parole chiave)