This study compares the proposed hybrid method (wavelet robust M-estimation) to traditional ordinary least square) when there are de-noising or outlier problems for estimating multiple linear regression models using statistical criterion root mean square error (RMSE). According simulated and real data, is better than classical (Wavelet Ordinary Least Square) more accurate. The of less Wavelet S...

SUMMARY A novel method for automatically creating an optimum direction-of-arrival (DOA) estimation algorithm for a given radio environment using a genetic algorithm (GA) is proposed. DOA estimation algorithms are generally described by parameters and operators. The performance of a DOA estimation algorithm is evaluated using root mean square error (RMSE) through computer simulations. A GA searc...

MPEG-encoded variable bit rate (VBR) video traffic is expected to incur cell losses during its transmission over a broadband network. These cell losses introduce distortion into the received video. Frame drop rate and Root Mean Square Error (RMSE) can be used to objectively measure this distortion. These two metrics are useful in identifying the threshold cell loss rate (CLR) beyond which the q...

In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discha...

An operating temperature of the photovoltaic (PV) module greatly affects performance and its lifetime. Therefore, it is essential to evaluate in different weather conditions how performance. The primary objective this paper present a dynamic thermo-electric model for determining output power module. presented validated with field measurement at Institute Energy Technology, Faculty University Ma...

In order to improve the accuracy of grain production forecasting, this study proposed a new combination forecasting model, the model combined stepwise regression method with RBF neural network by assigning proper weights using inverse variance method. By comparing different criteria, the result indicates that the combination forecasting model is superior to other models. The performance of the ...

with regard to the importance of instantaneous peak discharge estimation for watershed management study, and due to the lack of and unqualified climatic and hydrologic data for estimation and measurement in countries such as iran, researchers were obliged to establish a link between constant parameters (geomorphologic) and variables (hydrologic) to present models with minimum dependence on clim...

In this work one year hourly solar radiation data are analyzed and modeled. Using a 2-D surface fitting approach, a novel model is developed for the general behavior of the solar radiation. The mathematical formulation of the 2-D surface model is obtained. The accuracy of the analytical surface model is tested and compared with another surface model obtained from a feed-forward Neural Network(N...

This study evaluated the accuracy of boreal forest above-ground biomass (AGB) and volume estimates obtained using airborne laser scanning (ALS) and RapidEye data in a two-phase sampling method. Linear regression-based estimation was employed using an independent validation dataset and the performance was evaluated by assessing the bias and the root mean square error (RMSE). In the phase I, ALS ...

This study investigated pilots' taxi performance, situation awareness and workload while taxiing with three different head-up display (HUD) symbology formats: Command-guidance, Situationguidance and Hybrid. Command-guidance symbology provided the pilot with required control inputs to maintain centerline position; Situation-guidance symbology provided conformal, scenelinked navigation informatio...