Wiener-Hammerstein Model Identification-Recursive lgorithms

Recursive algorithms for parameter estimation of Wiener-Hammerstein (W-H)models are developed. These algorithms are derived on the basis of minimizing cost functions of the output errors, the equation errors, and the prediction errors. Convergence conditions of the developed algorithms are established. Numerical results, to test the algorithms and illustrate their estimation accuracy, are given

Wiener-Hammerstein Model Identification-Recursive lgorithms

Recursive algorithms for parameter estimation of Wiener-Hammerstein (W-H)models are developed. These algorithms are derived on the basis of minimizing cost functions of the output errors, the equation errors, and the prediction errors. Convergence conditions of the developed algorithms are established. Numerical results, to test the algorithms and illustrate their estimation accuracy, are given

Parameter Estimation Of Wiener-Hammerstein Models

Several algorithms, namely the Output Error (OE), the Equation Error (EE), the Prediction Error (PE), and the Instrumental Variable (IV), for parameter estimation of Wiener-Hammerstein models, are derived. Monte Carlo simulation results are provided showing the effectiveness, and comparing the performance of these algorithm

Parameter Estimation Of Wiener-Hammerstein Models

Several algorithms, namely the Output Error (OE), the Equation Error (EE), the Prediction Error (PE), and the Instrumental Variable (IV), for parameter estimation of Wiener-Hammerstein models, are derived. Monte Carlo simulation results are provided showing the effectiveness, and comparing the performance of these algorithm

Accounting for asset retirement obligations and environmental liabilities

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Don Quijote de la Mancha. Lyhennelmä, suomi

Nimiösivulla myös: Espanjalaisten taiteilijain tekemillä kuvilla varustettu. Suomennettu lyhennetystä ranskalaisesta painoksesta.Suomentanut O.A. Joutsen.Kannen signeeraus: F.G.Å