Results 31 to 40 of about 418,071 (190)
IEEE Access, 2018 Reinforcement learning (RL) is an important machine learning paradigm that can be used for learning from the data obtained by the human-computer interface and the interaction in human-centered smart systems. One of the essential problems in RL algorithms
Dazi Li +3 more
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A Least-Squares Method for Sparse Low Rank Approximation of Multivariate Functions [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2015 In this paper, we propose a low-rank approximation method based on discrete least-squares for the approximation of a multivariate function from random, noisy-free observations. Sparsity inducing regularization techniques are used within classical algorithms for low-rank approximation in order to exploit the possible sparsity of low-rank approximations.
M. Chevreuil, R. Lebrun, A. Nouy, P. Rai
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SensorsIn this paper, the optimal approximation algorithm is proposed to simplify non-linear functions and/or discrete data as piecewise polynomials by using the constrained least squares.
Jieun Song, Bumjoo Lee
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Mathematics, 2023 In order to obtain the numerical results of 3D convection-diffusion-reaction problems with variable coefficients efficiently, we select the improved element-free Galerkin (IEFG) method instead of the traditional element-free Galerkin (EFG) method by ...
Heng Cheng, Zebin Xing, Yan Liu
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Coding Prony’s method in MATLAB and applying it to biomedical signal filtering
BMC Bioinformatics, 2018 Background The response of many biomedical systems can be modelled using a linear combination of damped exponential functions. The approximation parameters, based on equally spaced samples, can be obtained using Prony’s method and its variants (e.g.
A. Fernández Rodríguez +5 more
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A computationally efficient non‐iterative four‐parameter sine fitting method
IET Signal Processing, 2021 A computationally efficient four‐parameter least squares (LS) sine fitting method in the time domain is presented here. Unlike the most widespread procedure defined in the relevant IEEE standard, the proposed fitting is non‐iterative. This is achieved by
Balázs Renczes, Vilmos Pálfi
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A Robust Method for Gaussian Profile Estimation in the Case of Overlapping Objects
IEEE Access, 2021 The precise stellar object identification is one of the major research fields in astronomy. In astronomical images, the 2D Gaussian function provides a good approximation of stellar objects.
Anita Gribl, Davor Petrinovic
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Labor-capital relations in the construction sector in Poland [PDF]
Urbanism. Arhitectura. Constructii, 2015 The aim of this paper is to estimate the Cobb-Douglas production function and the CES (constant elasticity of substitution) function for the construction sector in Poland. The period since 2002 is analyzed.
Krzysztof Drachal
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FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
Applied Sciences, 2023 We propose a direct digital frequency synthesizer (DDFS) by using an error-controlled piecewise linear (PWL) approximation method. For a given function and a preset max absolute error (MAE), this method iterates continuously from right to left within the
Xuan Liao +4 more
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EURASIP Journal on Advances in Signal ProcessingThe work is devoted to a problem of the rhythm function estimation of a cyclic random process, which is based on the least squares approximation methods instead of well-known interpolation approach. Analytical dependencies between errors of estimation of
Serhii Lupenko +2 more
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