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On change point detection using the fused lasso method
In this paper we analyze the asymptotic properties of l1 penalized maximum likelihood estimation of signals with piece-wise constant mean values and/or variances. The focus is on segmentation of aExpand
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Analyzing iterations in identification with application to nonparametric H∞-norm estimation
TLDR
An iterative approach for nonparametric H∞-norm estimation that addresses both additive stochastic disturbances and input normalization. Expand
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Robust optimal experiment design for system identification
TLDR
This paper develops the idea of min-max robust experiment design for dynamic system identification and proposes a convex optimisation algorithm. Expand
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A Note on the SPICE Method
TLDR
In this article, we analyze the SPICE method developed in , and establish its connections with other standard sparse estimation methods such as the Lasso and the LAD-Lasso. Expand
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Model predictive control with integrated experiment design for output error systems
TLDR
We combine MPC with experiment design to formulate a control problem where excitation constraints are included. Expand
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On optimal input design for nonlinear FIR-type systems
TLDR
We consider optimal input design for system identification of nonlinear FIR-type systems in the prediction error (PEM) framework. Expand
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Sparse estimation based on a validation criterion
  • C. Rojas, H. Hjalmarsson
  • Computer Science, Mathematics
  • IEEE Conference on Decision and Control and…
  • 1 December 2011
TLDR
A sparse estimator with close ties with the LASSO (least absolute shrinkage and selection operator) is analysed. Expand
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A graph theoretical approach to input design for identification of nonlinear dynamical models
TLDR
In this paper the problem of optimal input design for model identification is studied. Expand
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Data-driven H∞-norm estimation via expert advice
TLDR
H∞-norm estimation is a data-driven estimation method exploiting iterative input design, without requiring parametric modeling. Expand
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Experimental evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer
It is commonly observed that over the lifetime of most model predictive controllers, the achieved performance degrades over time. This effect can often be attributed to the fact that the dynamics ofExpand
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