Signal Generator Agnostic Moment Matching
We study the model-reduction problem by moment matching for linear and nonlinear systems in a data-driven setting. We show that reduced-order models can be directly computed from input-output data without requiring knowledge of the structure of the signal generator or its internal state. The reduced...
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| Vydáno v: | IEEE transactions on automatic control Ročník 70; číslo 11; s. 7493 - 7508 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9286, 1558-2523 |
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| Abstract | We study the model-reduction problem by moment matching for linear and nonlinear systems in a data-driven setting. We show that reduced-order models can be directly computed from input-output data without requiring knowledge of the structure of the signal generator or its internal state. The reduced-order models thus obtained match the moments of the unknown underlying system asymptotically. Our formulation provides a simple way to enforce additional constraints on the structure of the reduced-order model, which could be used to incorporate prior knowledge about the underlying system. In addition, we show that our method can be directly applied to a large class of linear and nonlinear time-delay systems with minimal modifications. Finally, we provide a simple algorithmic formulation that can be used directly with data, and demonstrate its effectiveness on a benchmark example-a nonlinear RC ladder circuit. |
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| AbstractList | We study the model-reduction problem by moment matching for linear and nonlinear systems in a data-driven setting. We show that reduced-order models can be directly computed from input–output data without requiring knowledge of the structure of the signal generator or its internal state. The reduced-order models thus obtained match the moments of the unknown underlying system asymptotically. Our formulation provides a simple way to enforce additional constraints on the structure of the reduced-order model, which could be used to incorporate prior knowledge about the underlying system. In addition, we show that our method can be directly applied to a large class of linear and nonlinear time-delay systems with minimal modifications. Finally, we provide a simple algorithmic formulation that can be used directly with data, and demonstrate its effectiveness on a benchmark example—a nonlinear RC ladder circuit. |
| Author | Bhattacharjee, Debraj Moreschini, Alessio Astolfi, Alessandro |
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| SubjectTerms | Computational modeling Data-driven algorithms Integrated circuit modeling Interpolation Linear systems Matching Mathematical models Model reduction moment matching nonlinear model reduction Nonlinear systems Reduced order models Reduced order systems Signal generators Steady-state Time delay systems Transfer functions |
| Title | Signal Generator Agnostic Moment Matching |
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