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Impact of forgetting on models of rolling mills
Dedecius, Kamil ; Jirsa, Ladislav
The research report deals with an analysis of various models for modelling of the cold sheet rolling process. It comprises a thorough analysis of a mass-flow model and its weaknesses, brief analysis of normalization impact on modelling and exhaustive analysis of 4 defined models with exponential and partial forgetting and their comparison to models without forgetting. The report ends with a computer-intensive search for new blackbox models.
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Stručné porovnání vybraných metod zapomínání parametrů
Dedecius, Kamil
This paper brings a comparison of three selected techniques for estimation of slowly varying parameters of input-output models. One of them is the exponential forgetting method, which is the most popular and simplest method, while another method the alternative forgetting is based on it. The third selected method is the partial forgetting, which presents a completely different approach to the slowly varying parameters. The comparison of these methods is based on a one-step ahead prediction of a predefined time series with models employing these forgetting methods. The prediction errors are then compared.
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Parciální zapomínání. Nová metoda sledování časově proměnných parametrů
Dedecius, Kamil ; Nagy, Ivan ; Kárný, Miroslav ; Pavelková, Lenka
Tracking of slowly varying parameters is an important task in the theory of adaptive systems. Majority of prediction and control algorithms, employing regression models like autoregression model (AR), autoregression model with exogenous inputs (ARX), autoregression model with moving average (ARMA) etc., assume a carefully defined model structure and correctly estimated parameters. Problems arise, when the model parameters vary in time. The problems of slowly time-varying model parameters were given a thorough attention. The proposed partial forgetting method tries to solve this issue by a new approach.
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