Results 41 to 50 of about 33,160 (233)

Deterministic-Stochastic Subspace Identification of the Modal Parameters of a Machine Tool During Milling

Journal of Manufacturing and Materials Processing
Modal analysis is a standard tool for evaluating the dynamic behaviour of machine tools. Since the dynamic behaviour can differ for operating and analysis conditions, the use of operational modal analysis for machine tools has been researched over the ...
Willy Reichert, Martin Kolouch, Jan Berthold, Steffen Ihlenfeldt, Max Engelmann   +4 more
doaj   +1 more source

Model-Free Predictive Anti-Slug Control of a Well-Pipeline-Riser [PDF]

Modeling, Identification and Control, 2016
Simplified linearized discrete time dynamic state space models are developed for a 3-phase well-pipeline-riser and tested together with a high fidelity dynamic model built in K-Spice and LedaFlow. In addition the Meglio pipeline-riser model is used as an
Christer Dalen, David Di Ruscio
doaj   +1 more source

A Stochastic Majorize-Minimize Subspace Algorithm for Online Penalized Least Squares Estimation

, 2016
Stochastic approximation techniques play an important role in solving many problems encountered in machine learning or adaptive signal processing. In these contexts, the statistics of the data are often unknown a priori or their direct computation is too
Emilie, Chouzenoux, Jean-Christophe, Pesquet   +1 more
core   +3 more sources

Experimental Modal Analysis of Angle Signals Based on the Stochastic Subspace Identification Method

NUST Journal of Engineering Sciences, 2022
This paper aims to verify the extraction of modal parameters from angle signals using the stochastic subspace identification (SSI) method. The use of angle signal-based mode shapes can reduce the loss of node information and enhance the robustness in ...
In-Ho Kim
doaj   +1 more source

Partial Realization Theory and System Identification Redux

, 2017
Some twenty years ago we introduced a nonstandard matrix Riccati equation to solve the partial stochastic realization problem. In this paper we provide a new derivation of this equation in the context of system identification. This allows us to show that
Lindquist, Anders
core   +1 more source

A robust probabilistic approach to stochastic subspace identification

Journal of Sound and Vibration
Modal parameter estimation of operational structures is often a challenging task when confronted with unwanted distortions (outliers) in field measurements. Atypical observations present a problem to operational modal analysis (OMA) algorithms, such as stochastic subspace identification (SSI), severely biasing parameter estimates and resulting in ...
O’Connell, B.J., Rogers, T.J.
openaire   +4 more sources

Subspace Identification Method for Combined Deterministic-Stochastic Bilinear Systems [PDF]

IFAC Proceedings Volumes, 2000
Abstract In this paper, a 'four-block' subspace system identification method for combined deterministic-stochastic bilinear systems is developed. Estimation of state sequences, followed by estimation of system matrices, is the central component of subspace identification methods.
Huixin Chen, Jan Maciejowski
openaire   +1 more source

Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain

Advanced Science, EarlyView.
A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio, Tommaso Gili, Andrea Gabrielli, Maurizio Mattia   +3 more
wiley   +1 more source

Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique

Journal of Modern Power Systems and Clean Energy, 2017
In this paper, a data-driven stochastic subspace identification (SSI-DATA) technique is proposed as an advanced stochastic system identification (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate
Deyou YANG, Guowei CAI, Kevin CHAN
doaj   +1 more source

Subspace System Identification of the Kalman Filter [PDF]

Modeling, Identification and Control, 2003
Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati ...
David Di Ruscio
doaj   +1 more source