Publications associées à Minimum mean square error

Coordinate-Descent Diffusion Learning by Networked Agents

Ali H. Sayed, Bicheng Ying, Chengcheng Wang

This paper examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gr ...

Ieee-Inst Electrical Electronics Engineers Inc2018

The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems

Martin Vetterli, Benjamin Bejar Haro, Marta Martinez-Camara

Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. In this case, the performance of pop ...

2017

Optimizing the parameterization of deep mixing and internal seiches in one-dimensional hydrodynamic models: a case study with Simstrat v1.3

Alfred Johny Wüest, Damien Bouffard, Robert Vincent Schwefel, Adrien Gaudard, Carl Love Mikael Råman Vinnå, Martin Schmid

This paper presents an improvement of a one-dimensional lake hydrodynamic model (Simstrat) to characterize the vertical thermal structure of deep lakes. Using physically based arguments, we refine the transfer of wind energy to basin-scale internal waves ( ...

Copernicus Gesellschaft Mbh2017

Estimation of pedestrian origin-destination demand in train stations

Michel Bierlaire, Flurin Silvan Hänseler, Nicholas Alan Molyneaux

We present a framework for estimating pedestrian demand within a train station. It takes into account ridership data, and various direct and indirect indicators of demand. Such indicators may include link flow counts, density measurements, survey data, his ...

Informs2017

Parameter identification of a lithium-ion cell single-particle model through non-invasive testing

Mario Paolone, Dimitri Torregrossa, Emil Namor

Physics-based models of electrochemical cells are of great interest for the future battery management systems (BMSs), due to their accuracy and capability to predict cell physical states. One of their main disadvantages, when compared to equivalent circuit ...

Elsevier Science Bv2017

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Xin Liu, Hanjie Pan, Feng Xue, Jiaqi Liu

Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on BDF algorithm, we develop a data-dr ...

Ieee2017

Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation

Mario Paolone, Andreas Martin Kettner

This paper demonstrates the feasibility of implementing real-time state estimators for active distribution networks in field-programmable gate arrays (FPGAs) by presenting an operational prototype. The prototype is based on a linear state estimator that us ...

Institute of Electrical and Electronics Engineers2017

Bone orientation and position estimation errors using Cosserat point elements and least squares methods: Application to gait

Kamiar Aminian, Arnaud Barré

The aim of this study was to analyze the accuracy of bone pose estimation based on sub-clusters of three skin-markers characterized by triangular Cosserat point elements (TCPEs) and to evaluate the capability of four instantaneous physical parameters, whic ...

Elsevier2017

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Minimum mean square error

Coordinate-Descent Diffusion Learning by Networked Agents

The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems

Optimizing the parameterization of deep mixing and internal seiches in one-dimensional hydrodynamic models: a case study with Simstrat v1.3

Estimation of pedestrian origin-destination demand in train stations

Parameter identification of a lithium-ion cell single-particle model through non-invasive testing

Optimized Wavelet Denoising for Self-Similar alpha-Stable Processes

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Bone orientation and position estimation errors using Cosserat point elements and least squares methods: Application to gait

Graph Chatbot

Chattez avec Graph Search

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Optimized Wavelet Denoising for Self-Similar alpha-Stable Processes

Coordinate-Descent Diffusion Learning by Networked Agents

Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation

The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Parameter identification of a lithium-ion cell single-particle model through non-invasive testing

Optimizing the parameterization of deep mixing and internal seiches in one-dimensional hydrodynamic models: a case study with Simstrat v1.3

Estimation of pedestrian origin-destination demand in train stations

Bone orientation and position estimation errors using Cosserat point elements and least squares methods: Application to gait