Linear observation based total least squares

Pan, Guangming; Zhou, Yuegui; Sun, Hao; Guo, Wei

doi:10.1179/1752270614y.0000000090

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…One of the advantages of our proposal is that the application of the transformations leads to a two-dimensional model. This advantage is clearly reflected in the following fact: it is possible to apply an SLR model in order to fit the data to a line in the plane [35]. It contrasts with studies which implement multiple linear regressions due to the existence of multiple predictor variables.…”

Section: Simple Linear Regressionmentioning

confidence: 99%

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

et al. 2020

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A fundamental issue of the software engineering economics is productivity. In this regard, one measure of software productivity is delivery speed. Software productivity prediction is useful to determine corrective activities, as well as to identify improvement alternatives. A type of software maintenance is enhancement. In this paper, we propose a data analytics-based software engineering algorithm called search method based on feature construction (SMFC) for predicting the delivery speed of software enhancement projects. The SMFC belongs to the minimalist machine learning paradigm, and as such it always generates a two-dimensional model. Unlike the usual data analytics methods, SMFC includes an original algorithmic training procedure, in which both the independent and dependent variables are considered for transformation. SMFC prediction performance is compared to those of statistical regression, neural networks, support vector regression, and fuzzy regression. To do this, seven datasets of software enhancement projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2017 were used. The validation method is leave-one-out cross validation, whereas absolute residuals have been chosen as the performance measure. The results indicate that the SMFC is statistically better than statistical regression. This fact represents an obvious advantage in favor of SMFC, because the other two methods are not statistically better than SMFC.

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Section: Simple Linear Regressionmentioning

confidence: 99%

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

et al. 2020

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“…The classical method, so-called generalized total least squares (TLS) [18,[20][21][22], is proposed as a parameter estimation technique for the EIV model when all elements in channel matrices are perturbed by i.i.d. Particularly, the WTLS [19,[23][24][25] became popular in the mathematics field during this decade since its estimator has a better statistical accuracy under more general noise assumptions.…”

Section: Eiv-based System Modelmentioning

confidence: 99%

EIV-Based Interference Alignment Scheme with CSI Uncertainties

Kong

Peng

Zhang

et al. 2015

Mathematical Problems in Engineering

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A novel interference alignment (IA) scheme based on the errors-in-variables (EIV) mathematic model has been proposed to overcome the channel state information (CSI) estimation error for the MIMO interference channels. By solving an equivalently unconstrained optimization problem, the proposed IA scheme employing a weighted total least squares (WTLS) algorithm can obtain the solution to a constrained optimization problem for transmit precoding (TPC) matrices and minimizes the distortion caused by imperfect CSI according to the EIV model. It is shown that the design of TPC matrices can be realized through an efficient iterative algorithm. The convergence of the proposed scheme is presented as well. Simulation results show that the proposed IA scheme is robust over MIMO interference channels with imperfect CSI, which yields significantly better sum rate performance than the existing IA schemes such as distributed iterative IA, maximum signal-to-interference-plus-noise ratio (Max SINR), and minimum mean square error (MMSE) schemes.

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“…The first step in applying GPS data locally requires the determination of transformation parameters. The most widely used methods in literature for such an application include the similarity models of Bursa-Wolf, Molodensky-Badekas, Veis model and three-dimensional Affine (Ge et al 2013;Pan et al 2015;Solomon 2013;Zeng 2014Zeng , 2015Ziggah et al 2013). However, the major point here is that, before these aforementioned similarity models could be applied, there is the need to convert all geodetic data of common points to cartesian coordinates.…”

Section: Introductionmentioning

confidence: 99%

Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z)

Ziggah

et al. 2016

Math Geosci

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The standard forward transformation equation plays a major role in coordinate transformation between global and local datums. Thus, it is a prerequisite step in the forward conversion of geodetic coordinates into cartesian coordinates in coordinate transformation from global to local datum and vice versa. Numerous studies have been carried out on converting cartesian coordinates to geodetic coordinates (reverse procedure) through the application of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on literature covered pertaining to this study, it was realized that the existing researches do not fully address the issue of applying and testing alternative techniques in the case of the forward conversion. Hence, the purpose of this present study was to explore the coordinate conversion performance of two different artificial neural network approaches (backpropagation artificial neural network (BPANN) and radial basis function neural network (RBFNN)) and multiple linear regression (MLR). The statistical findings revealed that the BPANN, RBFNN and MLR offered satisfactory prediction of cartesian coordinates. However, the RBFNN compared to BPANN and MLR showed better stability and more accurate prediction results. Furthermore, in terms of maximum three-dimensional position error, the RBFNN attained 0.004 m while 0.011 and 0.627 m were achieved, respectively, by MLR and BPANN. By virtue of the success achieved in this study, the main conclusion drawn here is that RBFNN provides a promising alter-B Hu Youjian 123 Math Geosci native in the forward conversion of geodetic coordinates into cartesian coordinates. Therefore, the capability of artificial neural network as a powerful tool for solving majority of function approximation problems in geodesy has been demonstrated.

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Linear observation based total least squares

Cited by 10 publications

References 21 publications

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

EIV-Based Interference Alignment Scheme with CSI Uncertainties

Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z)

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