Results 31 to 40 of about 1,606 (181)

Adaptive Kernel Learning Kalman Filtering With Application to Model-Free Maneuvering Target Tracking

IEEE Access, 2022
Kernel method is a non-parametric linearization method for system modeling, which uses nonlinear projection from input data space to high-dimensional Hilbert feature space and employs kernel function for hiding the projection operator in a linear learner
Yuankai Li, Jiaxin Lou, Xiaosu Tan, Yanke Xu, Jinpeng Zhang, Zhongliang Jing   +5 more
doaj   +1 more source

Numerical Solutions of the Second-Order One-Dimensional Telegraph Equation Based on Reproducing Kernel Hilbert Space Method

Abstract and Applied Analysis, 2013
We investigate the effectiveness of reproducing kernel method (RKM) in solving partial differential equations. We propose a reproducing kernel method for solving the telegraph equation with initial and boundary conditions based on reproducing kernel ...
Mustafa Inc, Ali Akgül, Adem Kılıçman   +2 more
doaj   +1 more source

New Numerical Method for Solving Tenth Order Boundary Value Problems

Mathematics, 2018
In this paper, we implement reproducing kernel Hilbert space method to tenth order boundary value problems. These problems are important for mathematicians. Different techniques were applied to get approximate solutions of such problems.
Ali Akgül, Esra Karatas Akgül, Dumitru Baleanu, Mustafa Inc   +3 more
doaj   +1 more source

Reproducing kernel functions for linear tenth-order boundary value problems

ITM Web of Conferences, 2018
Higher order differential equations have always been an onerous problem to investigate for the mathematicians and engineers. Different numerical methods were applied to get numerical approximations of such problems.
Akgül Ali, Akgül Esra Karatas, Orcan Baris, Inc Mustafa   +3 more
doaj   +1 more source

Numerical solvability of generalized Bagley–Torvik fractional models under Caputo–Fabrizio derivative

Advances in Difference Equations, 2021
This paper deals with the generalized Bagley–Torvik equation based on the concept of the Caputo–Fabrizio fractional derivative using a modified reproducing kernel Hilbert space treatment.
Shatha Hasan, Nadir Djeddi, Mohammed Al-Smadi, Shrideh Al-Omari, Shaher Momani, Andreea Fulga   +5 more
doaj   +1 more source

ORBIT‐AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age‐Related Macular Degeneration in Multi‐Cohorts

Advanced Science, EarlyView.
Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui, Dejia Wen, Patrick Yu‐Wai‐Man, Xiaorong Li   +3 more
wiley   +1 more source

A new kernel method for the uniform approximation in reproducing kernel Hilbert spaces

Applied Mathematics Letters
This paper discussed the uniform approximation of functions on reproducing kernel Hilbert spaces (RKHS). In this direction, classical approximation methods are investigated by Fourier orthogonal projections (assuming that the Fourier coefficients are given) and their discrete versions (assuming that function values are well-distributed).
Themistoclakis, Woula, Van Barel, Marc
openaire   +2 more sources

Numerical Algorithm for the Third-Order Partial Differential Equation with Three-Point Boundary Value Problem

Abstract and Applied Analysis, 2014
A numerical method based on the reproducing kernel theorem is presented for the numerical solution of a three-point boundary value problem with an integral condition.
Jing Niu, Ping Li
doaj   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Numerical technique for solving physical models using reproducing kernel Hilbert space method with purely integral conditions

Boundary Value Problems
In this work, we investigate the Klein–Gordon equation, a physical problem, using the reproducing kernel Hilbert space method (RKHSM). The analytical solution is expressed as a series within the reproducing kernel Hilbert space (RKHS).
Hadjer Zerouali, Nourhane Attia, Ali Akgül, Ahcene Merad, Douha Saadi, Evren Hincal, Murad Khan Hassani   +6 more
doaj   +1 more source