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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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 70; no. 11; pp. 7493 - 7508
Main Authors: Bhattacharjee, Debraj, Moreschini, Alessio, Astolfi, Alessandro
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary: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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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2025.3576063