Reference Evapotranspiration (ET0) is a basic parameter for determination of irrigation program. It is one of the most important factors in water resources management and considers as a requirement for every irrigation and drainage plans. Because of the spatial variations of ET0, it is necessary to use from intrapolation methods in order to estimation of this parameter in regional studie. Using...

The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the LMS algorithm. We present in this paper an multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the stan...

The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the RLS algorithm. We present in this paper n multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the stand...

Propagation of radio occultation (RO) signals through the lower troposphere results in high phase acceleration and low signal to noise ratio signal. The excess Doppler estimation accuracy in lower troposphere is very important in receiving RO signals which can be estimated by sliding window spectral analysis. To do this, various frequency estimation methods such as MUSIC and ESPRIT can be adopt...

Parameter estimation is a key problem in the development of process models, both steadyand unsteadystate, and thus is an important issue in both process design and control. The error-in-variable approach differs distinctly from the standard approach in that measurement errors in both dependent and independent system variables are taken into account when formulating the objective function in the...

Estimating the error of classification and regression models is one of the most crucial tasks in machine learning. While the global error is capable to measure the quality of a model, local error estimates are even more interesting: on the one hand they contribute to better understanding of prediction models (where does and where does not work the model well), on the other hand they may provide...

Recently, Kulikov [1] presented the idea of double quasi-consistency, which facilitates global error estimation and control, considerably. More precisely , a local error control implemented in such methods plays a part of global error control at the same time. However, Kulikov studied only Nordsieck formulas and proved that there exists no doubly quasi-consistent scheme among those methods. In ...