Parsimonious System Identification from Fragmented Quantized Measurements
Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear irreversible process, which makes the traditional methods of system identification no longer applicable. In this work, we propose a method fo...
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| Published in | International journal of control Vol. 97; no. 8; pp. 1770 - 1779 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-7179 |
| DOI | 10.1080/00207179.2023.2228943 |
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| Abstract | Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear irreversible process, which makes the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious linear time invariant system identification when only quantized observations, discerned from noisy data, are available. More formally, given a priori information on the system, represented by a compact set containing the poles of the system, and quantized realizations, our algorithm aims at identifying the least order system that is compatible with the available information. The proposed approach takes also into account that the available data can be subject to fragmentation. Our proposed algorithm relies on an ADMM approach to solve a
,
, quasi-norm objective problem. Numerical results highlight the performance of the proposed approach when compared to the
minimization in terms of the sparsity of the induced solution. |
|---|---|
| AbstractList | Quantization is the process of mapping an input signal from an infinite
continuous set to a countable set with a finite number of elements. It is a
non-linear irreversible process, which makes the traditional methods of system
identification no longer applicable. In this work, we propose a method for
parsimonious linear time invariant system identification when only quantized
observations, discerned from noisy data, are available. More formally, given a
priori information on the system, represented by a compact set containing the
poles of the system, and quantized realizations, our algorithm aims at
identifying the least order system that is compatible with the available
information. The proposed approach takes also into account that the available
data can be subject to fragmentation. Our proposed algorithm relies on an ADMM
approach to solve a ℓp, 0<p<1, quasi-norm objective problem. Numerical
results highlight the performance of the proposed approach when compared to the
ℓ1 minimization in terms of the sparsity of the
induced solution. Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear irreversible process, which makes the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious linear time invariant system identification when only quantized observations, discerned from noisy data, are available. More formally, given a priori information on the system, represented by a compact set containing the poles of the system, and quantized realizations, our algorithm aims at identifying the least order system that is compatible with the available information. The proposed approach takes also into account that the available data can be subject to fragmentation. Our proposed algorithm relies on an ADMM approach to solve a , , quasi-norm objective problem. Numerical results highlight the performance of the proposed approach when compared to the minimization in terms of the sparsity of the induced solution. Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear irreversible process, which makes the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious linear time invariant system identification when only quantized observations, discerned from noisy data, are available. More formally, given a priori information on the system, represented by a compact set containing the poles of the system, and quantized realizations, our algorithm aims at identifying the least order system that is compatible with the available information. The proposed approach takes also into account that the available data can be subject to fragmentation. Our proposed algorithm relies on an ADMM approach to solve a ℓ p , 0 < p < 1 , quasi-norm objective problem. Numerical results highlight the performance of the proposed approach when compared to the ℓ 1 minimization in terms of the sparsity of the induced solution.Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear irreversible process, which makes the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious linear time invariant system identification when only quantized observations, discerned from noisy data, are available. More formally, given a priori information on the system, represented by a compact set containing the poles of the system, and quantized realizations, our algorithm aims at identifying the least order system that is compatible with the available information. The proposed approach takes also into account that the available data can be subject to fragmentation. Our proposed algorithm relies on an ADMM approach to solve a ℓ p , 0 < p < 1 , quasi-norm objective problem. Numerical results highlight the performance of the proposed approach when compared to the ℓ 1 minimization in terms of the sparsity of the induced solution. |
| Author | Lagoa, Constantino M Sleem, Omar M |
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| Keywords | Quantization System identification Sparsity ADMM |
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| Snippet | Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear... Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear... |
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| Title | Parsimonious System Identification from Fragmented Quantized Measurements |
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