Suchergebnisse - pseudo-linear regression algorithms

  1. 1

    Some remarks on the bias distribution analysis of discrete-time identification algorithms based on pseudo-linear regressions von Vau, Bernard, Bourlès, Henri

    ISSN: 0167-6911, 1872-7956
    Veröffentlicht: Elsevier B.V 01.09.2018
    Veröffentlicht in Systems & control letters (01.09.2018)
    “… ), not for pseudo-linear regression (PLR) ones, for which we give the correct frequency domain bias analysis, both in open- and closed-loop …”
    Volltext
    Journal Article
  2. 2

    On the convergence of pseudo-linear regression algorithms von STOICA, PETRE, SÖDERSTRÖM, TORSTEN, AHLÉN, ANDERS, SOLBRAND, GÓTE

    ISSN: 0020-7179, 1366-5820
    Veröffentlicht: London Taylor & Francis Group 01.01.1985
    Veröffentlicht in International journal of control (01.01.1985)
    “… The convergence properties of a general iterative (off-line) pseudo-linear regression (PLR …”
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    Journal Article
  3. 3

    On the asymptotic accuracy of pseudo-linear regression algorithms von STOICA, P, SODERSTROM, T, AHLEN, A, SOLBRAND, G

    ISSN: 0020-7179, 1366-5820
    Veröffentlicht: London Taylor & Francis Group 01.01.1984
    Veröffentlicht in International journal of control (01.01.1984)
    “… The accuracy properties of a general pseudo-linear regression (PLR) method are examined …”
    Volltext
    Journal Article
  4. 4

    Existence of stationary points for pseudo-linear regression identification algorithms von Regalia, P.A., Mboup, M., Ashari, M.

    ISSN: 0018-9286
    Veröffentlicht: New York, NY IEEE 01.05.1999
    Veröffentlicht in IEEE transactions on automatic control (01.05.1999)
    “… The authors prove the existence of a stable transfer function satisfying the nonlinear equations characterizing an asymptotic stationary point, in undermodeled cases, for a class of pseudo-linear …”
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    Journal Article
  5. 5

    Some remarks on the bias distribution analysis of discrete-time identification algorithms based on pseudo-linear regressions von Vau, Bernard, Bourlès, Henri

    ISSN: 2331-8422
    Veröffentlicht: Ithaca Cornell University Library, arXiv.org 18.06.2018
    Veröffentlicht in arXiv.org (18.06.2018)
    “… ), not for pseudo-linear regression (PLR) ones, for which we give the correct frequency domain bias analysis, both in open- and closed-loop …”
    Volltext
    Paper
  6. 6

    Gradient-based iterative parameter estimation for Box–Jenkins systems von Wang, Dongqing, Yang, Guowei, Ding, Ruifeng

    ISSN: 0898-1221, 1873-7668
    Veröffentlicht: Elsevier Ltd 01.09.2010
    Veröffentlicht in Computers & mathematics with applications (1987) (01.09.2010)
    “… –Jenkins systems with finite measurement input/output data. Compared with the pseudo-linear regression stochastic gradient approach, the proposed algorithm updates …”
    Volltext
    Journal Article
  7. 7

    Identification for Precision Mechatronics: An Auxiliary Model‐Based Hierarchical Refined Instrumental Variable Algorithm von Zhang, Chen, Liu, Yang, Liu, Kaixin, Song, Fazhi

    ISSN: 1049-8923, 1099-1239
    Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 01.08.2025
    “… Based on the maximum likelihood principle, the optimality conditions for the proposed identification algorithms are formulated for ACTARMA systems …”
    Volltext
    Journal Article
  8. 8

    Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems von Wang, Xuehai, Ding, Feng

    ISSN: 0890-6327, 1099-1115
    Veröffentlicht: Bognor Regis Blackwell Publishing Ltd 01.06.2016
    “… stochastic gradient algorithm, for pseudolinear multivariate systems and proves that the parameter estimation errors consistently converge to zero under persistent excitation conditions …”
    Volltext
    Journal Article
  9. 9

    Multiinnovation Least-Squares Identification for System Modeling von Feng Ding, Liu, Peter X, Guangjun Liu

    ISSN: 1083-4419, 1941-0492, 1941-0492
    Veröffentlicht: United States IEEE 01.06.2010
    “… A multiinnovation least-squares (MILS) identification algorithm is presented for linear regression models with unknown parameter vectors by expanding the innovation length in the traditional recursive least-squares (RLS …”
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    Journal Article
  10. 10

    Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms von Maduranga, M. W. P., Tilwari, Valmik, Abeysekera, Ruvan

    ISSN: 2314-7172, 2314-7172
    Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
    “… Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms …”
    Volltext
    Journal Article
  11. 11

    Recursive computational formulas of the least squares criterion functions for scalar system identification von Ma, Junxia, Ding, Rui

    ISSN: 0307-904X
    Veröffentlicht: Elsevier Inc 01.01.2014
    Veröffentlicht in Applied mathematical modelling (01.01.2014)
    “… The proposed recursive computation formulas can be extended to the estimation algorithms of the pseudo-linear regression models for equation error systems and output error systems …”
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    Journal Article
  12. 12

    A New Absolute Encoder Design Based on Piecewise Pseudo-Linear Signals von Celik, Emre, Obdan, Atiye Hulya

    ISSN: 1530-437X, 1558-1748
    Veröffentlicht: New York IEEE 15.08.2024
    Veröffentlicht in IEEE sensors journal (15.08.2024)
    “… by conventional sine-cosine encoders. The obtained pseudo-linear sections are made more linear through third-order polynomial regression …”
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    Journal Article
  13. 13

    Link Adaptation on an Underwater Communications Network Using Machine Learning Algorithms: Boosted Regression Tree Approach von Alamgir, M.S.M., Sultana, Mst. Najnin, Chang, Kyunghi

    ISSN: 2169-3536, 2169-3536
    Veröffentlicht: Piscataway IEEE 2020
    Veröffentlicht in IEEE access (2020)
    “… Interest in the study of next-generation underwater sensor networks for ocean investigations has increased owing to developing concerns over their utilization …”
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    Journal Article
  14. 14

    An l2-stable feedback structure for nonlinear adaptive filtering and identification von Sayed, Ali H., Rupp, Markus

    ISSN: 0005-1098, 1873-2836
    Veröffentlicht: Oxford Elsevier Ltd 1997
    Veröffentlicht in Automatica (Oxford) (1997)
    “… in IIR modeling and more recent results in H ∞ theory. In particular, two algorithms due to Feintuch and to Landau, as well as the so-called pseudo-linear regression and Gauss-Newton algorithms, are discussed within the framework proposed here …”
    Volltext
    Journal Article
  15. 15

    Pseudo-linear regression identification based on generalized orthonormal transfer functions: Convergence conditions and bias distribution von Vau, Bernard, Bourlès, Henri

    ISSN: 2331-8422
    Veröffentlicht: Ithaca Cornell University Library, arXiv.org 13.08.2019
    Veröffentlicht in arXiv.org (13.08.2019)
    “… This result is specific to pseudo-linear regression properties, and cannot be transposed to most of prediction error method algorithms …”
    Volltext
    Paper
  16. 16

    Acoustic echo cancelation using a pseudo-linear regression and QR-decomposition von Harteneck, M., Stewart, R.W.

    ISBN: 9780780330733, 0780330730
    Veröffentlicht: IEEE 1996
    “… In this paper the problem of acoustic echo cancelation is addressed using an adaptive IIR filtering algorithm based on a QR decomposition and a pseudo-linear regression …”
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    Tagungsbericht
  17. 17

    Adaptive IIR filtering using QR matrix decomposition von Harteneck, M., Stewart, R.W.

    ISSN: 1053-587X
    Veröffentlicht: New York, NY IEEE 01.09.1998
    Veröffentlicht in IEEE transactions on signal processing (01.09.1998)
    “… In this correspondence, an approach to adaptive IIR filtering based on a pseudo-linear regression and applying an iterative QR matrix decomposition is developed …”
    Volltext
    Journal Article
  18. 18

    A new algorithm for state estimation of stochastic linear discrete systems von Ahmed, M.S.

    ISSN: 0018-9286
    Veröffentlicht: New York, NY IEEE 01.08.1994
    Veröffentlicht in IEEE transactions on automatic control (01.08.1994)
    “… The procedure performs explicit minimization of the innovation variance and is based upon the principle of pseudo linear regression (PLR) method …”
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    Journal Article
  19. 19

    New approach for kinetic parameters determination for hydrothermal oxidation reaction von Mateos, David, Portela, Juan R., Mercadier, Jacques, Marias, Frédéric, Marraud, Christine, Cansell, François

    ISSN: 0896-8446, 1872-8162
    Veröffentlicht: Elsevier B.V 01.05.2005
    Veröffentlicht in The Journal of supercritical fluids (01.05.2005)
    “… °C and at a constant pressure of 25 MPa. Three different methods, namely pseudo-first-order kinetics, multiple linear regression and Runge …”
    Volltext
    Journal Article
  20. 20

    Artificial neural network models for fault detection and isolation of industrial processes von Józef Korbicz, Andrzej Janczak

    ISSN: 2299-3649, 2956-5839
    Veröffentlicht: Institute of Fundamental Technological Research Polish Academy of Sciences 01.02.2023
    “… The paper focuses on using of artificial neural networks in model-based fault detection and isolation. Modelling of a system both at its normal operation …”
    Volltext
    Journal Article