نتایج جستجو برای: error functions

تعداد نتایج: 727925  

One of the most important issues in forest biometrics is the use of allometric functions to estimate the tree height by using diameter-height models. Measuring the total height of trees is usually a complex and time-consuming process. In allometric functions, the diameter is measured directly but the height of the tree is an estimate of an allometric model, which will be more accurate if the cr...

Journal: :Remote Sensing 2017
Yang Zhang Zhengqiang Li Lili Qie Weizhen Hou Zhihong Liu Ying Zhang Yisong Xie Xingfeng Chen Hua Xu

Aerosol optical depth (AOD) is a widely used aerosol optical parameter in atmospheric physics. To obtain this parameter precisely, many institutions plan to launch satellites with multi-angle measurement sensors, but one important step in aerosol retrieval, the estimation of surface reflectance, is still a pressing issue. This paper presents an AOD retrieval method based on the multi-angle inte...

B. Mirzaeian, M. Moallem, V. Tahani and Caro Lucas,

In this paper, a new method based on genetic-fuzzy algorithm for multi-objective optimization is proposed. This method is successfully applied to several multi-objective optimization problems. Two examples are presented: the first example is the optimization of two nonlinear mathematical functions and the second one is the design of PI controller for control of an induction motor drive supplie...

Reduction of the quality of the image formed by an optical system is a function of different parameters such as lens aberrations, CCD digitization errors, and the errors of system assembling. Assembling errors usually consist of two types: 1) the prism error, which is the error of non-orthogonality of the optical axis and the image plane 2) the de-centering error, which is error of not passing ...

B. Mirzaeian, M. Moallem, V. Tahani and Caro Lucas,

In this paper, a new method based on genetic-fuzzy algorithm for multi-objective optimization is proposed. This method is successfully applied to several multi-objective optimization problems. Two examples are presented: the first example is the optimization of two nonlinear mathematical functions and the second one is the design of PI controller for control of an induction motor drive supplie...

In this paper‎, ‎a modern method is presented to solve a class of fractional optimal control problems (FOCPs) indirectly‎. ‎First‎, ‎the necessary optimality conditions for the FOCP are obtained in the form of two fractional differential equations (FDEs)‎. ‎Then‎, ‎the unknown functions are approximated by the hybrid functions‎, ‎including Bernoulli polynomials and Block-pulse functions based o...

The issue of pressure sensitivity of anisotropic sheet metals is investigated with introducing two new non-AFR criteria which are called here linear and non-Linear pressure sensitive criteria. The yield and plastic potential functions of these criteria are calibrated with directional tensile/compressive yield stresses and directional tensile Lankford coefficients, respectively. To determine unk...

2007
Vladimir Atanasovski Liljana Gavrilovska

WLANs, due to the characteristics of the wireless medium, exhibit time and location dependent features. The ability to adapt to such conditions and enable optimal behavior is a key issue in future generation heterogeneous networks where WLANs are envisioned as an integral part. This paper uses a recently derived analytical model of the throughput performance of non-saturated contending IEEE 802...

2013
Shiyao Liu Huaiqing Wu William Q. Meeker

The joint probability density function, evaluated at the observed data, is commonly used as the likelihood function to compute maximum likelihood estimates. For some models, however, there exist paths in the parameter space along which this densityapproximation likelihood goes to infinity and maximum likelihood estimation breaks down. In applications, all observed data are discrete due to the r...

2004
Tetsuro Takahashi Kozo Nawata Kentaro Inui Yuji Matsumoto

The system we presented for the subtask1 and subtask2 in QAC2 is based on our previous one [12], which utilized a greedy answer seeking model using paraphrasing. We incorporate a re-ranking model for matching questions and passages into the previous system. In the model, we integrated a proximitybased scoring function with the structural-based scoring function. Unfortunately, the result of eval...

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