Relevant bibliographies by topics / Gaussian interpolation function

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Gaussian interpolation function'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Gaussian interpolation function"

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Sydorenko, Yuliia V., and Mykola V. Horodetskyi. "Modification of the Algorithm for Selecting a Variable Parameter of the Gaussian Interpolation Function." Control Systems and Computers, no. 6 (290) (December 2020): 21–28. http://dx.doi.org/10.15407/csc.2020.06.021.

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The paper presents an algorithm for selecting the optimal value of the variable parameter α of the Gaussian interpolation function to obtain the smallest possible error when interpolating the tabular data. The results of the algorithm are checked on a sample of elementary mathematical functions. For comparison, the interpolation data of the Lagrange polynomial are given. The paper presents the results of Gaussian interpolation at different α, conclusions are made about the need to applying the algorithm for selecting of its optimal value.
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Sydorenko, I., and M. Horodetskyi. "ANALYSIS OF GAUSSIAN INTERPOLATION FUNCTION ALGORITHM ON ELEMENTARY ALGEBRAIC FUNCTIONS." Modern problems of modeling 19 (September 8, 2020): 134–45. http://dx.doi.org/10.33842/2313-125x/2020/19/134/145.

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Dutra e Silva Júnior, Élvio Carlos, Leandro Soares Indrusiak, Weiler Alves Finamore, and Manfred Glesner. "A Programmable Look-Up Table-Based Interpolator with Nonuniform Sampling Scheme." International Journal of Reconfigurable Computing 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/647805.

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Interpolation is a useful technique for storage of complex functions on limited memory space: some few sampling values are stored on a memory bank, and the function values in between are calculated by interpolation. This paper presents a programmable Look-Up Table-based interpolator, which uses a reconfigurable nonuniform sampling scheme: the sampled points are not uniformly spaced. Their distribution can also be reconfigured to minimize the approximation error on specific portions of the interpolated function’s domain. Switching from one set of configuration parameters to another set, selecte
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Seleznjev, Oleg. "Large deviations in the piecewise linear approximation of Gaussian processes with stationary increments." Advances in Applied Probability 28, no. 02 (1996): 481–99. http://dx.doi.org/10.1017/s0001867800048588.

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We consider the piecewise linear interpolation of Gaussian processes with continuous sample paths and stationary increments. The interrelation between the smoothness of the incremental variance function, d(t – s) = E[(X(t) – X(s))2], and the interpolation errors in mean square and uniform metrics is studied. The method of investigation can also be applied to the analysis of different methods of interpolation. It is based on some limit results for large deviations of a sequence of Gaussian non-stationary processes and related point processes. Non-stationarity in our case means mainly the local
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Seleznjev, Oleg. "Large deviations in the piecewise linear approximation of Gaussian processes with stationary increments." Advances in Applied Probability 28, no. 2 (1996): 481–99. http://dx.doi.org/10.2307/1428068.

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We consider the piecewise linear interpolation of Gaussian processes with continuous sample paths and stationary increments. The interrelation between the smoothness of the incremental variance function, d(t – s) = E[(X(t) – X(s))2], and the interpolation errors in mean square and uniform metrics is studied. The method of investigation can also be applied to the analysis of different methods of interpolation. It is based on some limit results for large deviations of a sequence of Gaussian non-stationary processes and related point processes. Non-stationarity in our case means mainly the local
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Hartman, Eric, and James D. Keeler. "Predicting the Future: Advantages of Semilocal Units." Neural Computation 3, no. 4 (1991): 566–78. http://dx.doi.org/10.1162/neco.1991.3.4.566.

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In investigating gaussian radial basis function (RBF) networks for their ability to model nonlinear time series, we have found that while RBF networks are much faster than standard sigmoid unit backpropagation for low-dimensional problems, their advantages diminish in high-dimensional input spaces. This is particularly troublesome if the input space contains irrelevant variables. We suggest that this limitation is due to the localized nature of RBFs. To gain the advantages of the highly nonlocal sigmoids and the speed advantages of RBFs, we propose a particular class of semilocal activation fu
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Khalili, Mohammad Amin, and Behzad Voosoghi. "Gaussian Radial Basis Function interpolation in vertical deformation analysis." Geodesy and Geodynamics 12, no. 3 (2021): 218–28. http://dx.doi.org/10.1016/j.geog.2021.02.004.

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Shen, Qiang, and Longzhi Yang. "Generalisation of Scale and Move Transformation-Based Fuzzy Interpolation." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 3 (2011): 288–98. http://dx.doi.org/10.20965/jaciii.2011.p0288.

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Fuzzy interpolative reasoning has been extensively studied due to its ability to enhance the robustness of fuzzy systems and reduce system complexity. In particular, the scale and move transformation-based approach is able to handle interpolation with multiple antecedent rules involving triangular, complex polygon, Gaussian and bell-shaped fuzzy membership functions [1]. Also, this approach has been extended to deal with interpolation and extrapolation with multiple multi-antecedent rules [2]. However, the generalised extrapolation approach based on multiple rules may not degenerate back to th
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Lee, Lung-fei. "INTERPOLATION, QUADRATURE, AND STOCHASTIC INTEGRATION." Econometric Theory 17, no. 5 (2001): 933–61. http://dx.doi.org/10.1017/s0266466601175043.

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This paper considers features in numerical and stochastic integration approaches for the evaluation of analytically intractable integrals. It provides a unification of these two approaches. Some important features in quadrature formulations, namely, interpolation and region partition, can provide a valuable device for the design of a stochastic simulator. An interpolating function can be used as a valuable control variate for variance reduction in simulation. We illustrate possible variance reduction by some numerical cases with Gaussian quadrature. The resulting simulator may also be regarded
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Platte, Rodrigo B., and Tobin A. Driscoll. "Polynomials and Potential Theory for Gaussian Radial Basis Function Interpolation." SIAM Journal on Numerical Analysis 43, no. 2 (2005): 750–66. http://dx.doi.org/10.1137/040610143.

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Dissertations / Theses on the topic "Gaussian interpolation function"

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Городецький, Микола Вадимович. "Вплив коефіцієнта згладжування на вигляд інтерполяційної функції Гауса". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/36376.

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Робота присвячена аналізу впливу коефіцієнта згладжування на вигляд інтерполяційної функції Гауса. В першому розділі наведено методи розв’язання задач обробки масивів даних методами інтерполяції, в другому розділі описується теоретичні засоби класичних методів інтерполяції та інтерполяційної функції Гауса, в третьому розділі описується аналіз порівняльних результатів та проведено аналіз похибки із застосуванням варіативного коефіцієнта згладжування, в четвертому розділі описується вибір засобів програмної реалізації, в п’ятому розділі описується архітектура та методика роботи користувача з про
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Larsson-Cohn, Lars. "Gaussian structures and orthogonal polynomials." Doctoral thesis, Uppsala : Matematiska institutionen, Univ. [distributör], 2002. http://publications.uu.se/theses/91-506-1535-1/.

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Ji, Wei. "Spatial Partitioning and Functional Shape Matched Deformation Algorithm for Interactive Haptic Modeling." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1226364059.

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Bah, Ebrima M. "Numerické metody pro rekonstrukci chybějící obrazové informace." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-401582.

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The Diploma thesis deals with reconstruction of Missing data of an Image. It is done by the use of appropriate Mathematical theory and numerical algorithm to reconstruct missing information. The result of this implementation is the reconstruction of missing image information. The thesis also compares different numerical methods, and see which one of them perform best in terms of efficiency and accuracy of the given problem, hence it is used for the reconstruction of missing data.
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Book chapters on the topic "Gaussian interpolation function"

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Turgut, Umut Orcun, and Didem Gokcay. "Shape Preservation Based on Gaussian Radial Basis Function Interpolation on Human Corpus Callosum." In Spectral and Shape Analysis in Medical Imaging. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51237-2_10.

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"Towards a Gausslet analysis: Gaussian representations of functions." In Function Spaces, Interpolation Theory and Related Topics, edited by Michael Cwikel, Miroslav Englis, Alois Kufner, Lars-Erik Persson, and Gunnar Sparr. Walter de Gruyter, 2002. http://dx.doi.org/10.1515/9783110198058.425.

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"On Gaussian-summing identity maps between Lorentz sequence spaces." In Function Spaces, Interpolation Theory and Related Topics, edited by Michael Cwikel, Miroslav Englis, Alois Kufner, Lars-Erik Persson, and Gunnar Sparr. Walter de Gruyter, 2002. http://dx.doi.org/10.1515/9783110198058.383.

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Conference papers on the topic "Gaussian interpolation function"

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Yamaguchi, Takuro, Masaaki Ikehara, and Yasuhiro Nakajima. "Image interpolation based on weighting function of Gaussian." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421329.

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Planas, Robert, Nicholas Oune, and Ramin Bostanabad. "Extrapolation With Gaussian Random Processes and Evolutionary Programming." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22381.

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Abstract Emulation plays an indispensable role in engineering design. However, the majority of emulation methods are formulated for interpolation purposes and their performance significantly deteriorates in extrapolation. In this paper, we develop a method for extrapolation by integrating Gaussian processes (GPs) and evolutionary programming (EP). Our underlying assumption is that there is a set of free-form parametric bases that can model the data source reasonably well. Consequently, if we can find these bases via some training data over a region, we can do predictions outside of that region
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Davis, Sean, Oishik Sen, Gustaaf Jacobs, and H. S. Udaykumar. "Coupling of Micro-Scale and Macro-Scale Eulerian-Lagrangian Models for the Computation of Shocked Particle-Laden Flows." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-62521.

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The accuracy and efficiency of several algorithms that couple output from full resolution micro-scale Direct Numerical Simulation computations to input for macro-scale Eulerian-Lagrangian (EL) methods for the computation of high-speed, particle-laden flow are assessed. A Stochastic Collocation method, a Gaussian Radial Basis Function (RBF) Artificial Neural Network (ANN), and an improved RBF-ANN are compared for the fitting of an analytical drag coefficient formula that depends on Mach number and Reynolds number. The improved RBF-ANN uses a clustering algorithm to enhance conditioning of inter
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Luo, Huageng, Liping Wang, Don Beeson, and Gene Wiggs. "Pseudo-ARMA Model for Meta-Modeling Extrapolation." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27208.

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In spite of exponential growth in computing power, the enormous computational cost of complex and large-scale engineering design problems make it impractical to rely exclusively on original high fidelity simulation codes. Therefore, there has been an increasing interest in the use of fast executing meta-models to alleviate the computational cost required by slow and expensive simulation models — especially for optimization and probabilistic design. However, many state-of-the-art meta-modeling techniques, such as Radial Basis Function (RBF), Gaussian Process (GP), and Kriging can only make good
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McCartney, Michael, Matthias Haeringer, and Wolfgang Polifke. "Comparison of Machine Learning Algorithms in the Interpolation and Extrapolation of Flame Describing Functions." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91319.

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Abstract This paper examines and compares commonly used Machine Learning algorithms in their performance in interpolation and extrapolation of FDFs, based on experimental and simulation data. Algorithm performance is evaluated by interpolating and extrapolating FDFs and then the impact of errors on the limit cycle amplitudes are evaluated using the xFDF framework. The best algorithms in interpolation and extrapolation were found to be the widely used cubic spline interpolation, as well as the Gaussian Processes regressor. The data itself was found to be an important factor in defining the pred
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Strömberg, Niclas. "Reliability Based Design Optimization by Using a SLP Approach and Radial Basis Function Networks." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59522.

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In this paper reliability based design optimization by using radial basis function networks (RBFN) as surrogate models is presented. The RBFN are treated as regression models. By taking the center points equal to the sampling points an interpolation is obtained. The bias of the network is taken to be known a priori or posteriori. In the latter case, the well-known orthogonality constraint between the weights of the RBFN and the polynomial basis functions of the bias is adopted. The optimization is performed by using a first order reliability method (FORM)-based sequential linear programming (S
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Hunt, Terence D., and Steven C. Gustafson. "Digital image interpolation using adaptive Gaussian basis functions." In Electronic Imaging 2004, edited by Charles A. Bouman and Eric L. Miller. SPIE, 2004. http://dx.doi.org/10.1117/12.555598.

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Qian, Hao, and Yang Chen. "A New Method of Fuzzy Interpolative Reasoning Based on Gaussian-Type Membership Function." In 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicic.2009.34.

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Karri, Satyaprakash, John Charonko, and Pavlos Vlachos. "Robust Gradient Estimation Schemes Using Radial Basis Functions." In ASME 2008 Fluids Engineering Division Summer Meeting collocated with the Heat Transfer, Energy Sustainability, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/fedsm2008-55151.

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Utilization of Radial Basis Functions (RBFs) for gradient estimation is tested over various noisy flow fields. A novel mathematical formulation which minimizes the energy functional associated with the analytical surface fit for Gaussian (GA) and Generalized Multiquadratic (GMQ) RBFs is presented. Error analysis of the wall gradient estimation was performed at various resolutions, interpolation grid sizes, and noise levels in synthetically generated Poiseuille and Womersley flow fields for RBFs along with standard finite difference schemes. To test the effectiveness of the methods with DPIV (D
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Sarkar, S., K. Ghosh, and K. Bhaumik. "A Weighted Sum of Multi-scale Gaussians Generates New Near-ideal Interpolation Functions." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615959.

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