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Iterative solutions of min-max parameter estimation with bounded data uncertainties

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A Parameter Estimation Method for Fuel Cell Diagnostics

Jan Van Herle, Stefan Diethelm, Arata Nakajo, Leonidas Tsikonis

Researchers and developers typically use I-V curves (current vs. potential) as a means to demonstrate good data fitting of a model under experimental validation. Despite the popularity of this method, assessment of the results can be intuitive rather than ...

2011

On the role of diversity in sparsity estimation

Michael Christoph Gastpar

A major challenge in sparsity pattern estimation is that small modes are difficult to detect in the presence of noise. This problem is alleviated if one can observe samples from multiple realizations of the nonzero values for the same sparsity pattern. We ...

IEEE2011

Robust Value at Risk Prediction

Loriano Mancini, Fabio Trojani

This paper proposes a robust semiparametric bootstrap method to estimate predictive distributions of GARCH-type models. The method is based on a robust estimation of parametric GARCH models and a robustified resampling scheme for GARCH residuals that contr ...

Oxford University Press2011

Moving horizon estimation for distributed nonlinear systems with application to cascade river reaches

Giancarlo Ferrari Trecate, Marcello Farina

In this paper we consider a nonlinear constrained system observed by a sensor network and propose a distributed state estimation scheme based on moving horizon estimation (MHE). In order to embrace the case where the whole system state cannot be reconstruc ...

2011

Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

Ali H. Sayed

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton's method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of l ...

IEEE2010

Robust Multivariate and Nonlinear Time Series Models

Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables lik ...

EPFL2010

Distributed moving horizon estimation for nonlinear constrained systems

Giancarlo Ferrari Trecate, Marcello Farina

In this paper we consider a nonlinear constrained system observed by a sensor network and propose a distributed state estimation scheme based on Moving Horizon Estimation (MHE). In order to embrace the case where the whole system state cannot be reconstruc ...

2010

Distributed moving horizon estimation for nonlinear constrained systems

Giancarlo Ferrari Trecate, Marcello Farina

2010

How robust are robust schedules in reality?

Michel Bierlaire, Matteo Salani, Niklaus Eggenberg

Due to economic pressure industries, when planning, tend to focus on optimizing the expected profit or the yield. The consequence of highly optimized solutions is an increased sensitivity to uncertainty. This generates additional "operational" costs, incur ...

2010

Multi-level diffusion adaptive networks

Ali H. Sayed

We study the problem of distributed estimation, where a set of nodes are required to collectively estimate some parameter of interest from their measurements. Diffusion algorithms have been shown to achieve good performance, increased robustness and are am ...

IEEE2009