Results 41 to 50 of about 207,837 (263)

Asymptotic behaviour of the minimum bound method for choosing the regularization parameter [PDF]

, 1998
We consider a parameter choice method (called the minimum bound method) for regularization of linear ill-posed problems that was developed by Raus and Gfrerer for the case with continuous, deterministic data.
Lukas, M.A.
core  

Alzheimer's Disease Biomarkers Measured by Capillary Blood Collection Versus Venous Plasma and Cerebrospinal Fluid

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Blood biomarkers are increasingly critical for non‐invasive Alzheimer's Disease (AD) diagnostics and research but typically require on‐site processing. Remote point‐of‐care collection methods could enhance biomarker testing in clinical and research settings.
Sara Ho, Ziling Shen, Alexandria Lewis, Megha Patel, Anny Zheng, Gianna Proscino, Thaovy Ho, Jiangxia Wang, Marilyn Albert, Abhay Moghekar   +9 more
wiley   +1 more source

Global in time well-posedness of a three-dimensional periodic regularized Boussinesq system

Demonstratio Mathematica
Global in time weak solution to a regularized periodic three-dimensional Boussinesq system is proved to exist in energy spaces. This solution depends continuously on the initial data. In particular, it is unique.
Almutairi Shahah
doaj   +1 more source

A Regularized Interface Model for Simulating the Response of Adhesive Joints

Annals of "Dunarea de Jos" University of Galati, Fascicle XII, Welding Equipment and Technology
A regularized interface damage model is presented grounded on the cohesive-zone concept. This is obtained using a gradient-based formulation, which is equivalent to the introduction of the laplacian of a scalar damage field into the threshold function of
N. Valoroso
doaj   +1 more source

Comparisons Where It Matters: Using Layer-Wise Regularization to Improve Federated Learning on Heterogeneous Data

Applied Sciences, 2022
Federated Learning is a widely adopted method for training neural networks over distributed data. One main limitation is the performance degradation that occurs when data are heterogeneously distributed.
Ha Min Son, Moon Hyun Kim, Tai-Myoung Chung   +2 more
doaj   +1 more source

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality [PDF]

, 2004
A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules.
Harris, C. J., Hong, X., Harris, C.J., Chen, S.   +3 more
core   +1 more source

A Two‐Stage Plasma Biomarker Algorithm Reduces Amyloid PET Use and the p‐Tau217 Gray Zone

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Plasma p‐Tau217 leaves 30%–50% of patients in an indeterminate “gray zone” often requiring Aβ‐PET. We developed GRAD (Gatekeeper‐Reflex for Alzheimer's Disease), a two‐stage Aβ‐positivity prediction model to improve classification while reserving Aβ‐PET for residual uncertainty.
Harthik Parankusham, Casey Vanderlip, Colin Birkenbihl, Eashwar Krishna, Chizobam Ugboaja, Andrew E. Budson, Brandon Frank, for the Alzheimer's Disease Neuroimaging Initiative   +7 more
wiley   +1 more source

Temperature dependence of relaxation spectra for highly hydrated gluten networks [PDF]

, 2010
In the present investigation, the temperature dependence (0e50 C) of the relaxation spectrum of hydrated gluten was studied using novel numerical algorithms. Tikhonov regularization, in conjunction with the L-curve criterion for optimal calculation of
Kasapis, Stefan, Kontogiorgos, Vassilis, Stefan Kasapis, Vassilis Kontogiorgos   +3 more
core   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Robust Data-Reuse Regularized Recursive Least-Squares Algorithms for System Identification Applications

Sensors
The recursive least-squares (RLS) algorithm stands out as an appealing choice in adaptive filtering applications related to system identification problems.
Radu-Andrei Otopeleanu, Constantin Paleologu, Jacob Benesty, Laura-Maria Dogariu, Cristian-Lucian Stanciu, Silviu Ciochină   +5 more
doaj   +1 more source