Reduced‐order modeling methods for parametric bilinear systems via Walsh functions

Abstract This paper investigates model order reduction (MOR) methods for parametric bilinear systems using Walsh functions. First, the coefficient matrices are expanded via Taylor series to transform the system considered into a polynomial parametric system. The resulting system is then expanded using Walsh functions, where the bilinear terms are effectively handled, leading to a generalized Sylvester equation. Then, LU factorization combines with a hybrid iterative strategy to obtain the solution of the generalized Sylvester equation, which can significantly enhance the convergence rate of the generalized minimal residual method and reduce computational costs. Finally, orthogonal projection matrices are constructed from the obtained expansion coefficients to generate the parametric reduced‐order systems. Theoretical analysis shows that the reduced‐order systems can match the first several expansion coefficients of the output of the original system. Numerical experiments demonstrate the feasibility and effectiveness of the proposed methods.

Authors

Institutions

Publication Details

Journal
Asian Journal of Control
Published
2026-02-12
DOI
https://doi.org/10.1002/asjc.70087
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reduced‐order modeling methods for parametric bilinear systems via Walsh functions

Ping Yang, Ming‐Yang Wang, Yi‐Chu Feng, Zhao‐Hong Wang
Asian Journal of Control
Model Reduction and Neural Networks
article

Reduced‐order modeling methods for parametric bilinear systems via Walsh functions

Ping Yang, Ming‐Yang Wang, Yi‐Chu Feng, Zhao‐Hong Wang
article en

Abstract

Abstract This paper investigates model order reduction (MOR) methods for parametric bilinear systems using Walsh functions. First, the coefficient matrices are expanded via Taylor series to transform the system considered into a polynomial parametric system. The resulting system is then expanded using Walsh functions, where the bilinear terms are effectively handled, leading to a generalized Sylvester equation. Then, LU factorization combines with a hybrid iterative strategy to obtain the solution of the generalized Sylvester equation, which can significantly enhance the convergence rate of the generalized minimal residual method and reduce computational costs. Finally, orthogonal projection matrices are constructed from the obtained expansion coefficients to generate the parametric reduced‐order systems. Theoretical analysis shows that the reduced‐order systems can match the first several expansion coefficients of the output of the original system. Numerical experiments demonstrate the feasibility and effectiveness of the proposed methods.

Asian Journal of Control
Xinjiang University (CN)
Openalex Percentile: Top 55%
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Reduced‐order modeling methods for parametric bilinear systems via Walsh functions — Ping Yang, Ming‐Yang Wang, et al. · Asian Journal of Control (2026) | TGRS Research Map | TGRS