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Parameter Error (parameter + error)
Selected AbstractsMethods for the analysis of trends in streamflow response due to changes in catchment conditionENVIRONMETRICS, Issue 7 2001R. A. Letcher Abstract Two algorithms for analysing changes in streamflow response due to changes in land use and farm dam development, based on the Estimated Generalized Least Squares (EGLS) and the Generalized Additive Model (GAM) methods, were compared on three catchments in the Macquarie River Basin in NSW, Australia. In order to account for the influence of climatic conditions on streamflow response, the IHACRES conceptual rainfall-runoff model was calibrated on a daily time step over two-year periods then simulated over the entire period of concurrent rainfall, streamflow and temperature data. Residuals or differences between observed and simulated flows were calculated. The EGLS method was applied to a smoothing of the residual (daily) time series. Such residuals represent the difference between the simulated streamflow response to a fixed catchment condition (in the calibration period) and that due to the actual varying conditions throughout the record period. The GAM method was applied to quarterly aggregated residuals. The methods provided similar qualitative results for trends in residual streamflow response in each catchment for models with a good fitting performance on the calibration period in terms of a number of statistics, i.e. the coefficient of efficiency R2, bias and average relative parameter error (ARPE). It was found that the fit of the IHACRES model to the calibration period is critically important in determining trend values and significance. Models with well identified parameters and less correlation between rainfall and model residuals are likely to give the best results for trend analysis. Copyright © 2001 John Wiley & Sons, Ltd. [source] Estimating spatial and parameter error in parameterized nonlinear reaction,diffusion equationsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 9 2007B. R. Carnes Abstract A new approach is proposed for the a posteriori error estimation of both global spatial and parameter error in parameterized nonlinear reaction,diffusion problems. The technique is based on linear equations relating the linearized spatial and parameter error to the weak residual. Computable local element error indicators are derived for local contributions to the global spatial and parameter error, along with corresponding global error indicators. The effectiveness of the error indicators is demonstrated using model problems for the case of regular points and simple turning points. In addition, a new turning point predictor and adaptive algorithm for accurately computing turning points are introduced. Copyright © 2006 John Wiley & Sons, Ltd. [source] Modelling and simulation of a double-star induction machine vector control using copper-losses minimization and parameters estimationINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9 2002M.F. Mimouni Abstract This paper shows that it is possible to extend the principle of field-oriented control (FOC) approach to a double-star induction motor (DSIM). In the first stage, a robust variable structure current controller based on space phasor voltages PWM scheme is established. In this current controller design, only the stator currents and rotor speed sensors are used. In the second stage, the FOC method developed for DSIM is motivated by the minimization of the copper losses. The developed approach uses a loss model controller (LMC) and an adaptive rotor flux observer to compute the adequate rotor flux value minimizing the copper losses. The control variables are the stator currents or the machine input power. Compared to the constant rotor flux approach, it is proved that higher performances are achieved. However, the sensitivity of the FOC to parameter error of the machine still remains a problem. To guarantee the performance of the vector control, the stator and rotor resistances are adapted on-line, based on the Lyapunov theory. An appropriate choice of the reference model allows building a Lyapunov function by means of which the updating law can be found. The simulation results show the satisfactory behaviour of the proposed identification algorithm. Copyright © 2002 John Wiley & Sons, Ltd. [source] Demographic Issues in Longevity Risk AnalysisJOURNAL OF RISK AND INSURANCE, Issue 4 2006Eric Stallard Fundamental to the modeling of longevity risk is the specification of the assumptions used in demographic forecasting models that are designed to project past experience into future years, with or without modifications based on expert opinion about influential factors not represented in the historical data. Stochastic forecasts are required to explicitly quantify the uncertainty of forecasted cohort survival functions, including uncertainty due to process variance, parameter errors, and model misspecification errors. Current applications typically ignore the latter two sources although the potential impact of model misspecification errors is substantial. Such errors arise from a lack of understanding of the nature and causes of historical changes in longevity and the implications of these factors for the future. This article reviews the literature on the nature and causes of historical changes in longevity and recent efforts at deterministic and stochastic forecasting based on these data. The review reveals that plausible alternative sets of forecasting assumptions have been derived from the same sets of historical data, implying that further methodological development will be needed to integrate the various assumptions into a single coherent forecasting model. Illustrative calculations based on existing forecasts indicate that the ranges of uncertainty for older cohorts' survival functions will be at a manageable level. Uncertainty ranges for younger cohorts will be larger and the need for greater precision will likely motivate further model development. [source] | ||||||||||