Results 41 to 50 of about 18,405,059 (322)

Multivariate error function based neural network approximations

open access: yesJournal 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
doaj   +2 more sources

Error estimates for interpolation of rough data using the scattered shifts of a radial basis function

open access: yes, 2007
The error between appropriately smooth functions and their radial basis function interpolants, as the interpolation points fill out a bounded domain in R^d, is a well studied artifact. In all of these cases, the analysis takes place in a natural function
F.J. Narcowich   +8 more
core   +1 more source

Sigmoid functions for the smooth approximation to the absolute value function

open access: yesMoroccan Journal of Pure and Applied Analysis, 2021
We present smooth approximations to the absolute value function |x| using sigmoid functions. In particular, x erf(x/μ) is proved to be a better smooth approximation for |x| than x tanh(x/μ) and x2+μ\sqrt {{x^2} + \mu } with respect to accuracy.
Bagul Yogesh J., Chesneau Christophe
doaj   +1 more source

Predictions models with neural nets

open access: yesActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2008
The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way ...
Vladimír Konečný
doaj   +1 more source

Improved Prediction Model of the Friction Error of CNC Machine Tools Based on the Long Short Term Memory Method

open access: yesMachines, 2023
Friction is one of important factors that cause contouring errors, and the friction error is difficult to predict because of its nonlinearity. In this paper, a prediction model of the friction error of a servo system is proposed based on the Long Short ...
Tao Wang, Dailin Zhang
doaj   +1 more source

Concavity of the error function with respect to Hölder means

open access: yes, 2016
In this paper, we present a necessary and sufficient condition for the concavity of the error function with respect to Hölder means. Mathematics subject classification (2010): 33B20, 26E60.
Y. Chu, Tie-hong Zhao
semanticscholar   +1 more source

Nutritional and Behavioral Intervention for Long‐Term Childhood Acute Leukemia Survivors With Metabolic Syndrome

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Purpose Metabolic syndrome (MetS) is a common complication in survivors of childhood acute lymphoblastic and myeloid leukemia (AL), and a major risk factor for premature cardiovascular disease, type‐2‐diabetes, and metabolic dysfunction‐associated steatotic liver disease (MASLD).
Visentin Sandrine   +10 more
wiley   +1 more source

A Two-Domain MATLAB Implementation for Efficient Computation of the Voigt/Complex Error Function

open access: yesMathematics, 2022
In this work we develop a new algorithm for the efficient computation of the Voigt/complex error function. In particular, in this approach we propose a two-domain scheme where the number of the interpolation grid-points is dependent on the input ...
Sanjar M. Abrarov   +3 more
doaj   +1 more source

Solution of the Heat and Mass Transfer Problem for Soil Radiant Heating Conditions Using the Error Function [PDF]

open access: yesE3S Web of Conferences
Achieving high yields of agricultural crops requires the ability to predict soil temperature and moisture regimes, taking into account soil heating technology. The object of study is soil heated by a ceiling infrared emitter.
Pavlov Mikhail Vasilyevich   +8 more
doaj   +1 more source

Addressing Function Approximation Error in Actor-Critic Methods [PDF]

open access: yes, 2018
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.
Fujimoto, Scott   +2 more
core   +1 more source

Home - About - Disclaimer - Privacy