Identification of Additive Continuous-time Systems in Open and Closed loop
원문 초록
When identifying electrical, mechanical, or biological systems, parametric continuous-time identification methods can lead to interpretable and parsimonious models when the model structure aligns with the physical properties of the system. Traditional linear system identification may not consider the most parsimonious model when relying solely on unfactored transfer functions, which typically result from standard direct approaches. This paper presents a novel identification method that delivers additive models for both open and closed-loop setups. The estimators that are derived are shown to be generically consistent, and can admit the identification of marginally stable additive systems. Numerical simulations show the efficacy of the proposed approach, and its performance in identifying a modal representation of a flexible beam is verified using experimental data.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
The Interpretable and Effective Graph Neural Additive Networks
Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories
Forecasting time series with constraints
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data
Interpretable Generalized Additive Models for Datasets with Missing Values
DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.