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Accurate estimation and reliable universal performance of reference evapotranspiration (ET0) obtained from a few meteorological parameters are important for the rational planning of agricultural water resources and the effective management of water in irrigated regions. Meteorological data in southern China were used to calculate ET0 using the standard Penman-Monteith formula and determined the core decision variable (hours of sunshine, N) and the limited decision variable (relative humidity, RH) using path analysis. Estimation models using an artificial neural network and wavelet neural network were established for the Wuhan and Guangzhou meteorological stations. The statistical indices were positively correlated with the decision contribution rates to ET0. The ET0 values for other stations in southern China were all estimated by these models, which were trained for the Guangzhou station, and then made a total comparison with Hargreaves-Samani (HS) and Priestley-Taylor (PT) empirical ET0 models. Error analysis indicated that the root mean square error and the mean absolute per cent error were around 0.32 mm and 5.5%, respectively, with a high coefficient of determination and Nash-Sutcliffe efficiency over 0.9, indicating that these estimating models could be applied in more regions for universal analysis with high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we consider the following simple linear errors in variables modelwhere θ, β,x1,x2, … are unknown constants (parameters) and ξi, ηi,i=1, 2, … are observable. The consistency of the least square estimators for the unknown parameters β and θ is established, while the errors { ϵi}, {δi} are sequences of martingale difference. In order to obtain our results, some limit theorems for weighted sums of martingale differences are proved. [ABSTRACT FROM AUTHOR]

In this article, we consider the following linear modelwhere β is a q-dimensional unknown parametric vector, and {ξi} is a sequence of i.i.d. trial errors withand. Our purpose is to establish the moderate deviation principle for the estimator of the error variance σ2. [ABSTRACT FROM PUBLISHER]

In this article, the asymptotic normality and strong consistency of the least square estimators for the unknown parameters in the simple linear errors in variables model are established under the assumptions that the errors are stationary negatively associated sequences. [ABSTRACT FROM AUTHOR]

Zhang and Shang (2010) have presented the Uzawa-SOR (USOR) algorithmto solve augmented systems. In this paper, we establish a generalizedUzawa-SOR (GUSOR)method for solving augmented systems, which is the extension of the USOR method. We prove the convergence of the proposed method under suitable restrictions on the iteration parameters. Lastly, numerical experiments are carried out and experimental results show that our proposed method with appropriate parameters has faster convergence rate than the USOR method. [ABSTRACT FROM AUTHOR]

We introduce an iterative least squares method (ILS) for estimating the 2D-DOA and frequency based on L-shaped array. The ILS iteratively finds direction matrix and delay matrix, then 2D-DOA and frequency can be obtained by the least squares method. Without spectral peak searching and pairing, this algorithm works well and pairs the parameters automatically. Moreover, our algorithm has better performance than conventional ESPRIT algorithm and propagator method. The useful behavior of the proposed algorithm is verified by simulations. [ABSTRACT FROM AUTHOR]

Abstract: Process data measurements are important for process monitoring, control, optimization, and management decision making. However, process data may be heavily deteriorated by measurement biases and process leaks. Therefore, it is significant to simultaneously estimate biases and leaks with data reconciliation. In this paper, a novel strategy based on support vector regression (SVR) is proposed to achieve simultaneous data reconciliation and joint bias and leak estimation in steady processes. Although the linear objective function of the SVR approach proposed is robust with little computational burden, it would not result in the maximum likelihood estimate. Therefore, to ensure accurate estimates, the maximum likelihood estimate is applied based on the result of the SVR approach. Simulation and comparison results of a linear recycle system and a nonlinear heat-exchange network demonstrate that the proposed strategy is effective to achieve data reconciliation and joint bias and leak estimation with superior performances. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the following simple linear Errors-in-Variables (EV) regression model , , . The moderate deviation principle for the least squares (LS) estimators of the unknown parameters , in the model are obtained. [Copyright &y& Elsevier]

In this article, we obtain some deviation inequalities by non asymptotic approach, which extend the work of Liptser and Spokoiny (2000) in some sense, and apply them to the problem of the parameter estimation for more general regression models and Ito equations without any ergodic assumptions. In particular, for some special parameter estimation problems of discrete-time stochastic processes, we also give the more precise estimation. [ABSTRACT FROM AUTHOR]

Focusing shift of Bessel-Gauss (BG) beam with radial varying polarization is investigated by vector diffraction theory in this article. The radial phase modulation is given by a function of radial direction angle θ and m . It was found that the focal shift only can be altered considerably by the phase modulation parameter m , and the focus region intensity shape is variable with the change of the others parameters except m , turning into different shapes such as multiple intensity rings, dark hollow focus without the focus shift. There is an interesting phenomenon that the focus shifts along with the horizontal axis when the phase modulation parameter m is changing, and the shifts distance is linearity to the variation of the phase modulation parameter m. [ABSTRACT FROM AUTHOR]

Focusing properties of vector Bessel–Gauss (BG) beam with multiple-annular phase wavefront is investigated by vector diffraction theory in this article. The multiple-annular phase wavefront modulation is given by a function of radial direction angle and m. It was found that the focal properties can be altered considerably by the multiple-annular phase wavefront modulation parameter m, including the shape, size and focus shifts are variable with the change of parameters. There is an remarkable phenomenon that both the focal region and the gradient force distribution are symmetrical about the horizontal axis. In a word, the properties of the vector Bessel–Gauss (BG) beam with multiple-annular phase wavefront are considerably altered by the multiple-annular phase wavefront parameters, especially influenced the shape of focal region, and the quantity of intensity peak, but having no influence on the symmetry. [ABSTRACT FROM AUTHOR]

Abstract: Failures of power transformer are related with key-gas ratios C2H2/C2H4, CH4/H2 and C2H4/C2H6 strongly. Forecasting of these ratios of key-gas in power transformer oil is very significant to detect and identify incipient failures of transformer early. Forecasting of the ratios of key-gas in power transformer oil is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine with genetic algorithm (SVMG) is proposed to forecast the ratios of key-gas in power transformer oil, among which genetic algorithm (GA) is used to determine free parameters of support vector machine. The experimental results indicate that the SVMG method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. Consequently, the SVMG model is a proper alternative for forecasting of the ratios of key-gas in power transformer oil. [Copyright &y& Elsevier]