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    Wyświetlanie 1-6 z 6
    Tytuł:
    Least-squares estimation for a long-horizon performance index
    Autorzy:
    Janiszowski, K. B.
    Powiązania:
    https://bibliotekanauki.pl/articles/911215.pdf
    Data publikacji:
    2000
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    identyfikacja
    estymacja metodą najmniejszych kwadratów
    przewidywanie
    identification
    least squares estimation
    prediction
    recursive scheme
    Opis:
    Estimation of a parametric, discrete-time model for a SISO dynamic plant, derived for minimisation of a performance index determined as a sum of squared prediction errors within some time horizon is considered. A formula for a Long-Horizon Least-Squares (LHLS) off-line solution as well as a theorem for an LHLS recursive on-line scheme are derived. The LHLS scheme reveals some features of Least-Squares (LS) estimation and Instrumental-Variable (IV) estimation. An algorithm for the on-line LHLS scheme is presented and compared with LS and IV estimation schemes for a linear, second-order system. The fast convergence of the derived LHLS on-line scheme is demonstrated in the case of detecting changes in parameters of a non-stationary system.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2000, 10, 3; 559-573
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On parameter estimation in the bass model by nonlinear least squares fitting the adoption curve
    Autorzy:
    Marković, D.
    Jukić, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/331073.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Bass model
    least squares estimate
    existence problem
    data fitting
    model Bassa
    metoda najmniejszych kwadratów
    estymacja
    dopasowanie danych
    Opis:
    The Bass model is one of the most well-known and widely used first-purchase diffusion models in marketing research. Estimation of its parameters has been approached in the literature by various techniques. In this paper, we consider the parameter estimation approach for the Bass model based on nonlinear weighted least squares fitting of its derivative known as the adoption curve. We show that it is possible that the least squares estimate does not exist. As a main result, two theorems on the existence of the least squares estimate are obtained, as well as their generalization in the [...]. One of them gives necessary and sufficient conditions which guarantee the existence of the least squares estimate. Several illustrative numerical examples are given to support the theoretical work.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 1; 145-155
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The UD RLS Algorithm for Training Feedforward Neural Networks
    Autorzy:
    Bilski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908480.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    algorytm uczenia
    metoda najmniejszych kwadratów
    neural networks
    learning algorithms
    recursive least squares method
    UD factorization
    Opis:
    A new algorithm for training feedforward multilayer neural networks is proposed. It is based on recursive least squares procedures and U-D factorization, which is a well-known technique in filter theory. It will be shown that due to the U-D factorization method, our algorithm requires fewer computations than the classical RLS applied to feedforward multilayer neural network training.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2005, 15, 1; 115-123
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The role of parameter constraints in EE and OE methods for optimal identification of continuous LTI models
    Autorzy:
    Byrski, W.
    Byrski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/331405.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system ciągły
    funkcja przenoszenia
    metoda najmniejszych kwadratów
    continuous systems
    parameter constraints in identification
    modulating functions
    transfer function normalization
    least squares method
    Opis:
    The paper presents two methods used for the identification of Continuous-time Linear Time Invariant (CLTI) systems. In both methods the idea of using modulating functions and a convolution filter is exploited. It enables the proper transformation of a differential equation to an algebraic equation with the same parameters. Possible different normalizations of the model are strictly connected with different parameter constraints which have to be assumed for the nontrivial solution of the optimal identification problem. Different parameter constraints result in different quality of identification. A thorough discussion on the role of parameter constraints in the optimality of system identification is included. For time continuous systems, the Equation Error Method (EEM) is compared with the continuous version of the Output Error Method (OEM), which appears as a special sub-case of the EEM.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 379-388
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An efficient algorithm for estimating the parameters of superimposed exponential signals in multiplicative and additive noise
    Autorzy:
    Bian, J.
    Peng, H.
    Xing, J.
    Liu, Z.
    Li, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/331007.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    superimposed exponential signals
    modified Newton–Raphson algorithm
    multiplicative noise
    additive noise
    least squares estimator
    algorytm Newtona-Raphsona
    metoda najmniejszych kwadratów
    estymator
    Opis:
    This paper considers parameter estimation of superimposed exponential signals in multiplicative and additive noise which are all independent and identically distributed. A modified Newton–Raphson algorithm is used to estimate the frequencies of the considered model, which is further used to estimate other linear parameters. It is proved that the modified Newton–Raphson algorithm is robust and the corresponding estimators of frequencies attain the same convergence rate with Least Squares Estimators (LSEs) under the same noise conditions, but it outperforms LSEs in terms of the mean squared errors. Finally, the effectiveness of the algorithm is verified by some numerical experiments.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 1; 117-129
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
    Autorzy:
    Soltani, M.
    Chaari, A.
    Ben Hmida, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/330134.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    model rozmyty Takagi-Sugeno
    algorytm grupowania
    metoda najmniejszych kwadratów
    optymalizacja rojem cząstek
    Takagi-Sugeno fuzzy models
    noise clustering algorithm
    fuzzy c-regression model
    orthogonal least squares
    particle swarm optimization (PSO)
    Opis:
    This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 617-628
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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