Results 61 to 70 of about 2,581 (224)

A highly accurate numerical method for solving boundary value problem of generalized Bagley‐Torvik equation

Mathematical Methods in the Applied Sciences, EarlyView.
A highly accurate numerical method is given for the solution of boundary value problem of generalized Bagley‐Torvik (BgT) equation with Caputo derivative of order 0<β<2$$ 0<\beta <2 $$ by using the collocation‐shooting method (C‐SM). The collocation solution is constructed in the space Sm+1(1)$$ {S}_{m+1}^{(1)} $$ as piecewise polynomials of degree at ...
Suzan Cival Buranay, Mtema James Chin, Nazim I. Mahmudov   +2 more
wiley   +1 more source

Reproducing kernel Hilbert spaces

, 2021
U ovom radu upoznajemo se s teorijom Hilbertovih prostora reproducirajućih jezgri te objašnjavamo matematičku pozadinu primjene jezgrenih funkcija u umjetnoj inteligenciji. Istražujemo vezu između pozitivno semidefinitnih funkcija i jezgrenih funkcija te
Buljan, Antonio
core  

Reproducing Kernel Hilbert Spaces [PDF]

, 2019
This chapter introduces an elegant mathematical theory that has been developed for nonparametric regression with penalized estimation.
openaire   +2 more sources

Numerical algorithm to solve a coupled system of fractional order using a novel reproducing kernel method

Alexandria Engineering Journal, 2021
In this paper, a coupled system of fractional differential equations along with integral boundary conditions is discussed by means of the iterative reproducing kernel algorithm.
Rania Saadeh
doaj   +1 more source

Social Sustainability in Circular Bioeconomy Business Models: Insights From Argentina

Sustainable Development, EarlyView.
ABSTRACT Research on circular bioeconomy business models (CBEBM) has largely prioritised environmental and economic aspects, leaving out the social pillar. To address this gap, this paper analyses to what extent and in what ways social sustainability is integrated into CBEBM, based on 12 cases from northern Argentina, a region with high potential for ...
Celina N. Amato, Mónica Buraschi, Karen M. Siegel   +2 more
wiley   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source

A numerical method for solving conformable fractional integrodifferential systems of second-order, two-points periodic boundary conditions

Alexandria Engineering Journal, 2022
In this study, we will discuss numerical solutions of conformable fractional systems of second-order integrodifferential equations concerning couple types Volterra and Fredholm.
Nadjwa Berredjem, Banan Maayah, Omar Abu Arqub   +2 more
doaj   +1 more source

The oscillatory response of the electroretinogram and neuronal adaptation

Acta Ophthalmologica, EarlyView.
Abstract After more than 50 years, there still remains a challenge and an interest to know more as well as extend and deepen our understanding of the small rapid wavelets, the oscillatory potentials (OPs), of the electroretinogram (ERG) and the neuronal adaptation of the retina.
Lillemor Wachtmeister, Anders Eklund
wiley   +1 more source

Nonparametric Inference of Conditional Expectile Functions in Large‐Scale Time Series Data With Improved Efficiency

Journal of Time Series Analysis, EarlyView.
ABSTRACT Expectile is a coherent and elicitable law‐invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large‐scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting
Feipeng Zhang, Ping‐Shou Zhong
wiley   +1 more source

The $L^\infty$ Learnability of Reproducing Kernel Hilbert Spaces

CoRR, 2023
In this work, we analyze the learnability of reproducing kernel Hilbert spaces (RKHS) under the $L^\infty$ norm, which is critical for understanding the performance of kernel methods and random feature models in safety- and security-critical applications.
Hongrui Chen, Jihao Long, Lei Wu
openaire   +2 more sources