Results 11 to 20 of about 65,893 (254)
Multivariate error function based neural network approximations
Journal of Numerical Analysis and Approximation Theory, 2014 Here we present multivariate quantitative approximations of real and complex valued continuous multivariate functions on a box or \(\mathbb{R}^{N},\) \(N\in \mathbb{N}\), by the multivariate quasi-interpolation, Baskakov type and quadrature type neural ...
George A. Anastassiou
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Richards's curve induced Banach space valued multivariate neural network approximation. [PDF]
Arab J Math, 2023 AbstractHere, we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $${\mathbb {R}}^{N},$$ R N ,
Anastassiou GA, Karateke S.
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A Neural Network Approximation Based on a Parametric Sigmoidal Function
Mathematics, 2019 It is well known that feed-forward neural networks can be used for approximation to functions based on an appropriate activation function. In this paper, employing a new sigmoidal function with a parameter for an activation function, we consider a ...
Beong In Yun
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Machine Learning Alternatives to Response Surface Models
Mathematics, 2023 In the Design of Experiments, we seek to relate response variables to explanatory factors. Response Surface methodology (RSM) approximates the relation between output variables and a polynomial transform of the explanatory variables using a linear model.
Badih Ghattas, Diane Manzon
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IEEE Access, 2023 We analyze a fixed-point algorithm for reinforcement learning (RL) of optimal portfolio mean-variance preferences in the setting of multivariate generalized autoregressive conditional-heteroskedasticity (MGARCH) with a small penalty on trading.
Andrew Papanicolaou +3 more
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European Physical Journal C: Particles and Fields, 2022 We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments.
F. Abudinén +45 more
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Estimation of Approximating Rate for Neural Network inLwp Spaces
Journal of Applied Mathematics, 2012 A class of Soblove type multivariate function is approximated by feedforward network with one hidden layer of sigmoidal units and a linear output. By adopting a set of orthogonal polynomial basis and under certain assumptions for the governing activation
Jian-Jun Wang, Chan-Yun Yang, Jia Jing
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On sharpness of error bounds for multivariate neural network approximation [PDF]
Ricerche di Matematica, 2020 AbstractSingle hidden layer feedforward neural networks can represent multivariate functions that are sums of ridge functions. These ridge functions are defined via an activation function and customizable weights. The paper deals with best non-linear approximation by such sums of ridge functions.
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Scientific Reports, 2022 Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources.
Iman Ahmadianfar +4 more
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Neural Likelihoods via Cumulative Distribution Functions [PDF]
, 2020 We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs). By the application of automatic differentiation with respect to response variables and then
Chilinski, Pawel, Silva, Ricardo
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