Corpus ID: 14612237

DAISY : A database for identification of systems

@inproceedings{Moor1997DAISYA,
  title={DAISY : A database for identification of systems},
  author={B. Moor and P. D. Gersem and B. D. Schutter and W. Favoreel},
  year={1997}
}
We point out the existence of a disturbing deficiency in the field of system identification, namely the fact that many results, published in papers, are not reproducible. In many cases, datasets and time series, that are used to illustrate identification methods and algorithms in these publications, are not freely available. We propose to remedy this serious deficiency by setting up a publically accessible website, called DAISY, to which authors can submit datasets that are used to illustrate… Expand
State-of-the-Art and Evolution in Public Data Sets and Competitions for System Identification, Time Series Prediction and Pattern Recognition
TLDR
The role of data sets, benchmarks and competitions in the fields of system identification, time series prediction, classification, and pattern recognition are discussed in view of creating an environment of reproducible research. Expand
Algorithms for Subspace State-Space System Identification: An Overview
TLDR
A comparison between subspace identification and prediction error methods is made on the basis of computational complexity and precision of the methods by applying them on 10 industrial datasets. Expand
SUBSPACE SYSTEM IDENTIFICATION
TLDR
A comparison between subspace identification and prediction error methods is made on the basis of computational complexity and precision of methods by applying them to a glass tube manufacturing process. Expand
Three free data sets for development and benchmarking in nonlinear system identification
TLDR
Three sets of data suitable for development, testing and benchmarking of system identification algorithms for nonlinear systems are presented, collected from laboratory processes that can be described by block - oriented dynamic models, and by more general nonlinear difference and differential equation models. Expand
A resampling approach to estimate the stability of one-dimensional or multidimensional independent components
TLDR
It is demonstrated that the proposed reliability estimation can be used to discover stable one-dimensional or multidimensional independent components, to choose the appropriate BSS-model, to enhance significantly the separation performance, and, most importantly, to flag components that carry physical meaning. Expand
Comparative study between three subspace identification algorithms
TLDR
A direct comparison between CVA, N4SID and MOESP subspace system identification algorithms is made on the basis of 15 publicly available practical data sets on which the algorithms have been applied. Expand
Importance Sampling for Deep System Identification
TLDR
It is proved that using importance sampling schemes in system identification can provide a significant performance boost on a wide variety of systems, in particular when some of the system dynamic is only exhibited by relatively rare events. Expand
Data for benchmarking in nonlinear system identification
TLDR
Three sets of data suitable for development, testing and benchmarking of system identification algorithms for nonlinear systems are presented, collected from laboratory processes that can be described by block – oriented dynamic models, and by more general nonlinear difference and differential equation models. Expand
Reliability of ICA Estimates with Mutual Information
TLDR
A new highly accurate mutual information (MI) estimator is used to identify one- and multidimensional components or channels which are too close to Gaussians to be reliably separated. Expand
Extraction of Signals With Specific Temporal Structure Using Kernel Methods
TLDR
This work derives and evaluates a method for Blind Source Extraction in a reproducing kernel Hilbert space (RKHS) framework that is more robust than methods presented in the literature of BSE with respect to ambiguities in the available a priori information of the signal to be extracted. Expand
...
1
2
3
4
5
...