Results 11 to 20 of about 302,091 (291)
Using an Opportunity Matrix to Select Centers for RBF Neural Networks
Algorithms, 2023 When designed correctly, radial basis function (RBF) neural networks can approximate mathematical functions to any arbitrary degree of precision. Multilayer perceptron (MLP) neural networks are also universal function approximators, but RBF neural ...
Daniel S. Soper
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Zeros of Approximate Functional Approximations [PDF]
Mathematics of Computation, 1967 The gN(s) are of interest as approximations to the Riemann zeta function. Let s = or + it. In [1], it was shown that for t sufficiently large, gi(s) and g2(s) have their zeros on the critical line ar = 2. After encountering analytical difficulties in attempting to extend this theorem to further N, the calculations described below were undertaken.
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Linear and Quadratic Interpolators Using Truncated-Matrix Multipliers and Squarers
Computers, 2015 This paper presents a technique for designing linear and quadratic interpolators for function approximation using truncated multipliers and squarers. Initial coefficient values are found using a Chebyshev-series approximation and then adjusted through ...
E. George Walters III
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On the Universally Optimal Activation Function for a Class of Residual Neural Networks
AppliedMath, 2022 While non-linear activation functions play vital roles in artificial neural networks, it is generally unclear how the non-linearity can improve the quality of function approximations.
Feng Zhao, Shao-Lun Huang
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Mathematics, 2022 The paper considers the slope flow simulation and the problem of finding the optimal parameter values of this mathematical model. The slope flow is modeled using the finite volume method applied to the Reynolds-averaged Navier–Stokes equations with ...
Konstantin Barkalov +5 more
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Approximate Atomic Green Functions [PDF]
Molecules, 2021 In atomic and many-particle physics, Green functions often occur as propagators to formally represent the (integration over the) complete spectrum of the underlying Hamiltonian. However, while these functions are very crucial to describing many second- and higher-order perturbation processes, they have hardly been considered and classified for complex ...
Stephan Fritzsche, Andrey Surzhykov
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Approximately Subharmonic Functions [PDF]
Proceedings of the American Mathematical Society, 1952 for 0
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Uniform Approximation of Functions with Discrete Approximation Functionals
Journal of Approximation Theory, 1999 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chandler, Colston, Gibson, Archie G.
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THE SOLUTION OF THE APPROXIMATION PROBLEM OF NONLINEAR DEPENDANCES USING ARTIFICIAL NEURAL NETWORKS
Научный вестник МГТУ ГА, 2018 The paper discusses issues connected with the use of an artificial neural network (ANN) to approximate the experimental data. One of the problems in the development of the ANN is the choice of an appropriate activation function for neurons of the hidden ...
V. N. Ageyev
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Approximations to Kelvin Functions [PDF]
Mathematics of Computation, 1963 While preparing a digital computer program to examine the behavior of largetaper hub flanges, it was found necessary to use approximations to the Kelvin functions ber x, bei x, ker x, and kei x, and to their first derivatives. To obtain full machine accuracy, the approximations were required to be correct to nine significant figures.
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