945 resultados para O-W-F model


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The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Measurements of winter balance (bw) and summer balance (bs) have been carried out at Storbreen since 1949. Here we apply a simple mass balance model to study the climate sensitivity and to reconstruct the mass balance series prior to 1949. The model is calibrated and validated with data from an automatic weather station (AWS) operating in the ablation zone of Storbreen since 2001. Regression analysis revealed that bw was best modelled using precipitation data southwest of the glacier. Results from the model compared well with reported mass balance values for the period 1949-2006, obtained correlations (r) for bw and bs varied between 0.83 and 0.87 depending on model set up. Reconstruction of the mass balance series for the period 1924/1925-1948/1949 suggested a cumulative mass deficit of c. 30 m w.e. mainly due to highly negative summer balances, but also lower bw than the average for 1949-2006. Calculated change in specific mass balance for a ±1°C change in air temperature was ±0.55 m w.e., whereas a ±10 % increase in precipitation represented a change of ± 0.20 m w.e. Model results further indicated that for a 2°C warming, the ablation season will be extended by c. 30 days and that the period of ice melt at the AWS location will increase from c. 40 to c. 80 days.

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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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