The Experts below are selected from a list of 72291 Experts worldwide ranked by ideXlab platform
Nan Xiao - One of the best experts on this subject based on the ideXlab platform.
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A selective review and comparison for Interval Variable selection in spectroscopic modeling
Chemometrics and Intelligent Laboratory Systems, 2018Co-Authors: Li-li Wang, You-wu Lin, Xu-fei Wang, Nan XiaoAbstract:Abstract Dimension reduction and Variable selection are two types of effective methods that deal with high-dimensional data. In particular, Variable selection techniques are of wide-spread use and essentially consist of individual selection methods and Interval selection methods. Given the fact that the vibrational spectra have continuous features of spectral bands, Interval selection instead of individual spectral wavelength point selection allows for more stable models and easier interpretation. Numerous methods have been suggested for Interval selection recently. Therefore, this paper is devoted to a selective review on Interval selection methods with partial least squares (PLS) as the calibration model. We described the algorithms in the five classes: classic methods, penalty-based, sampling-based, correlation-based, and projection-based methods. Finally, we compared and discussed the performances of a subset of these methods on three real-world spectroscopic datasets.
F Tinloi - One of the best experts on this subject based on the ideXlab platform.
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probabilistic Interval analysis for structures with uncertainty
Structural Safety, 2010Co-Authors: Wei Gao, Chongmin Song, F TinloiAbstract:Abstract A hybrid probabilistic and Interval method for engineering problems described by a mixture of random and Interval Variables is presented. Random Interval arithmetic for carrying out basic operations between random and Interval Variables is developed by extending Interval arithmetic rules. The uncertainty of a random Interval Variable is represented by probabilistic as well as Interval information. A random Interval moment method is proposed to calculate the mean and variance of random Interval Variables. The solution strategy and associated numerical tool are developed by using perturbation theory and Taylor expansion for linear equations with random and Interval Variables. Engineering applications in structures with analytical or semi-analytical solutions are used to demonstrate the accuracy and effectiveness of the proposed method.
Li-li Wang - One of the best experts on this subject based on the ideXlab platform.
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A selective review and comparison for Interval Variable selection in spectroscopic modeling
Chemometrics and Intelligent Laboratory Systems, 2018Co-Authors: Li-li Wang, You-wu Lin, Xu-fei Wang, Nan XiaoAbstract:Abstract Dimension reduction and Variable selection are two types of effective methods that deal with high-dimensional data. In particular, Variable selection techniques are of wide-spread use and essentially consist of individual selection methods and Interval selection methods. Given the fact that the vibrational spectra have continuous features of spectral bands, Interval selection instead of individual spectral wavelength point selection allows for more stable models and easier interpretation. Numerous methods have been suggested for Interval selection recently. Therefore, this paper is devoted to a selective review on Interval selection methods with partial least squares (PLS) as the calibration model. We described the algorithms in the five classes: classic methods, penalty-based, sampling-based, correlation-based, and projection-based methods. Finally, we compared and discussed the performances of a subset of these methods on three real-world spectroscopic datasets.
Xuefeng Chen - One of the best experts on this subject based on the ideXlab platform.
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Interval Variable step-size spline adaptive filter for the identification of nonlinear block-oriented system
Nonlinear Dynamics, 2019Co-Authors: Liangdong Yang, Jinxin Liu, Zhibin Zhao, Ruqiang Yan, Xuefeng ChenAbstract:In order to improve the convergence speed of the nonlinear spline adaptive filter (SAF) in the identification of block-oriented systems, an Interval Variable step-size algorithm is proposed. Traditional SAF algorithm uses constant step size during iteration, leading to a contradiction between convergence speed and steady-state accuracy. In this paper, a new kind of Variable step-size algorithm is proposed, fully considering the particularity of spline interpolation in the nonlinear part of the block-oriented model. The step size of each interpolation Interval is independent from that of other Intervals, and it is dominated by the correlated squared error which is evaluated by an exponential-weighted averaging (EWA) process. In this paper, the independent step size in each interpolation Interval is also updated through an EWA process of the correlated error. The effects of the parameters on the convergence performance of the proposed strategy have been theoretically analyzed and verified by simulations. Finally, some numerical simulations have confirmed that the proposed Interval Variable step-size approach can significantly improve the convergence speed as well as reduce the steady-state error compared with the traditional SAF and the existing Variable step-size SAF algorithms.
Muhammad Saleem Kubar - One of the best experts on this subject based on the ideXlab platform.
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An efficient Variable selection method based on random frog for the multivariate calibration of NIR spectra
RSC Advances, 2020Co-Authors: Jingjing Sun, Wude Yang, Meichen Feng, Qifang Liu, Muhammad Saleem KubarAbstract:Variable selection is a critical step for spectrum modeling. In this study, a new method of Variable Interval selection based on random frog (RF), known as Interval Selection based on Random Frog (ISRF), is developed. In the ISRF algorithm, RF is used to search the most likely informative Variables and then, a local search is applied to expand the Interval width of the informative Variables. Through multiple runs and visualization of the results, the best informative Interval Variables are obtained. This method was tested on three near infrared (NIR) datasets. Four Variable selection methods, namely, genetic algorithm PLS (GA-PLS), random frog, Interval random frog (iRF) and Interval Variable iterative space shrinkage approach (iVISSA) were used for comparison. The results show that the proposed method is very efficient to find the best Interval Variables and improve the model's prediction performance and interpretation.
