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An Improved Fast Prediction Method for Full-Space Bistatic Acoustic Scattering of Underwater Vehicles. [PDF]

Sensors (Basel)
Gu R, Peng Z, Xue Y, Xu C, Chen C.
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Least Squares Approximation by Radial Functions

SIAM Journal on Mathematical Analysis, 1993
Summary: This paper is concerned with the study of continuous least squares approximation on a bounded domain in \(\mathbb{R}^ s\) by certain classes of radial functions. The approximating subspace is spanned by translates \(F(\cdot - x_ j)\) of a given radial function \(F\), where the (distinct) ``centers'' \(\{x_ j\}^ N_{j=1}\) are allowed to be ...
Quak, E., Sivakumar, N., Ward, J. D.
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Discrete least-squares radial basis functions approximations

Applied Mathematics and Computation, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Siqing Li, Leevan Ling, Ka Chun Cheung
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Least Squares Policy Evaluation Algorithms with Linear Function Approximation

Discrete Event Dynamic Systems, 2003
This paper deals with policy evaluation algorithms within the framework of infinite-horizon dynamic programming problems with discounted cost. The authors consider the discrete-time stationary Markov chain with state space \(\{1,2,\dots, n\}\) and the cost vector \(J\), given by \[ J(i)= E\Biggl[\sum^\infty_{t=0} \alpha^t g(i_t, i_{t+ 1})/i_0= i\Biggr],
Nedić, A., Bertsekas, D. P.
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Fuzzy basis functions, universal approximation, and orthogonal least-squares learning

IEEE Transactions on Neural Networks, 1992
Fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions. Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy ...
L X, Wang, J M, Mendel
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Least squares approximation of a function

International Journal of Mathematical Education in Science and Technology, 1996
The best linear approximation of a function f (x)near a point cis the tangent line at (c, f (c)).In spite of the well‐known measure of the error in the approximation in elementary calculus, it is not clear for how wide an interval about cis the tangent line the best.
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GaAs MESFET characterization using least squares approximation by rational functions

IEEE Transactions on Microwave Theory and Techniques, 1993
The authors propose a method of characterizing active devices such as the FET by describing S-parameters with a set of rational functions of angular frequency. The set of rational functions is uniquely determined by only 27 coefficients, while the conventional method using tabulated S-parameters requires eight times the number of sampling points (a ...
K. Nagatomo, Y. Daido, M. Shimizu, N. Okubo   +3 more
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Interval function and its linear least-squares approximation

Proceedings of the 2011 International Workshop on Symbolic-Numeric Computation, 2012
This paper reports an interval least-squares (ILS) algorithm that computationally approximates an interval function, in which both dependent and independent variables are interval valued. An initial version of this algorithm and its implementation were developed as a computational scheme for the stock market variability forecast with significantly ...
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Least squares surface approximation to scattered data using multiquadratic functions

Advances in Computational Mathematics, 1994
The paper documents an investigation into some methods for fitting surfaces to scattered data. The form of the fitting function is a multiquadratic function with the criteria for the fit being the least mean squared residual for the data points. The principal problem is the selection of knot points, or base points for the multiquadratic basis functions,
Franke, Richard, Hagen, Hans, Nielson, Gregory M.   +2 more
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Two-variable numeric function approximation using least-squares-based regression

2015 Nordic Circuits and Systems Conference (NORCAS): NORCHIP & International Symposium on System-on-Chip (SoC), 2015
Automated design of two-variable numeric functions can be realized efficiently by extending well-known multiplier-less linear function approximation techniques; the arithmetic signal processing effort is minimized by the utilization of a non-uniform piecewise segmentation scheme.
Jochen Rust, Nils Heidmann, Steffen Paul
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