| 1. Identity statement | |
| Reference Type | Book Section |
| Site | marte3.sid.inpe.br |
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identifier | 6qtX3pFwXQZ3r59YCT/H3JcM |
| Repository | sid.inpe.br/iris@1905/2005/08.04.01.31 (restricted access) |
| Last Update | 2018:01.10.16.34.11 (UTC) marciana |
| Metadata Repository | sid.inpe.br/iris@1905/2005/08.04.01.31.56 |
| Metadata Last Update | 2021:02.11.18.10.02 (UTC) administrator |
| Secondary Key | INPE-9497-PRE/5150 |
| Label | 10428 |
| Citation Key | SilvaSilv:2002:NePrCo |
| Title | Neural predictive contrl based on Kalman filtering algorithms  |
| Format | ISBN:85-900351-6-6 |
| Year | 2002 |
| Secondary Date | 20021011 |
| Access Date | 2024, May 08 |
| Secondary Type | PRE LN |
| Number of Files | 1 |
| Size | 181 KiB |
| 2. Context | |
| Author | 1 Silva, Atair Rios 2 Silva, Jaime da |
| Group | 1 DMC-INPE-MCT-BR |
| Editor | Balthazar, José Manoel Gonçalves, Paulo Batista Brasil, Reyolando M. F. L. R. F. Caldas, Iberê l. Rizatto, Felipe B. ed. |
| Book Title | Nonlinear dynamics, chaos, control and their applications to engineering sciences |
| Publisher | ABCM;SBMAC |
| Pages | cap. 5, 366-375 |
| Series Title | Nonlinear dynamics, chaos, control and their applications to engineering sciences |
| History (UTC) | 2014-09-29 15:09:03 :: administrator -> marciana :: 2002 2018-01-10 16:34:11 :: marciana -> administrator :: 2002 2021-02-11 18:10:02 :: administrator -> :: 2002 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Content Type | External Contribution |
| Keywords | ENGENHARIA E TECNOLOGIA ESPACIAL neural predictive control scheme Kalman filtering |
| Abstract | A neural predictive control scheme is considered where Kalman filtering is used not only to train the associated feedforward neural network modeling the dynamics but to also estimate the control. An approach is proposed in which the optimization of the predictive quadratic performance functional used to determine the discrete control actions is viewed in a typical iteration as a stochastic optimal linear parameter estimation problem. Direct analogy with Kalman filtering algorithms already developed for feedforward neural networks training allows the derivation of full non parallel as well as approximated parallel processing versions of Kalman filtering control algorithms. These algorithms are shown to be the result of applying Newton's Method to appropriate control optimization functionals and to provide solutions which converge to smooth and reference tracking controls. |
| Area | ETES |
| Arrangement | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDMC > Neural predictive contrl... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| Target File | 9497.pdf |
| User Group | administrator |
| Visibility | shown |
| Read Permission | deny from all and allow from 150.163 |
| Update Permission | not transferred |
| 5. Allied materials | |
| Next Higher Units | 8JMKD3MGPCW/446AF4B |
| Host Collection | sid.inpe.br/banon/2001/04.03.15.36 |
| 6. Notes | |
| Empty Fields | affiliation archivingpolicy archivist callnumber city copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition electronicmailaddress isbn issn language lineage mark mirrorrepository nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid rightsholder schedulinginformation secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype translator url versiontype volume |