Results 111 to 120 of about 3,141,474 (179)

Input–Output Finite‐Time Stability and Stabilization of Nonautonomous Nonlinear Quadratic Systems via State and Output Feedback

International Journal of Robust and Nonlinear Control, Volume 36, Issue 15, Page 7088-7101, October 2026.
ABSTRACT This paper presents novel results regarding the input–output finite‐time stability (IO‐FTS) of nonlinear quadratic systems (NLQSs), a class of dynamical models particularly useful for the study of robotic systems, biochemical reaction networks, and other processes of interest to the applied sciences.
Alessio Merola, Francesca Nesci, Donatella Dragone, Francesco Montefusco, Francesco Amato, Carlo Cosentino   +5 more
wiley   +1 more source

Revisiting damping identification: limitations and comparative evaluation under impulse, white noise, and seismic excitations

Frontiers in Built Environment
Eigensystem Realization Algorithm (ERA), Stochastic Subspace Identification (SSI), Continuous Wavelet Transform (CWT), and Enhanced Frequency Domain Decomposition (EFDD) are four widely used damping identification methods.
Yuanqi Zheng, Chin-Long Lee, Renjie Shen, Jia Guo   +3 more
doaj   +1 more source

An Integrated Gradient Boosting Machine Approach With Fisher's Discriminant Analysis: Empirical Assessment on Five Benchmark Datasets and Simulations

Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 5, October 2026.
ABSTRACT The aim of this study is to assess the impact of different dimensionality reduction techniques—specifically Principal Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (LDA)—on the classification performance of Gradient Boosting Machines (GBM).
Giulio Enzo Donninelli, Pietro Giorgio Lovaglio   +1 more
wiley   +1 more source

Forecasting linear dynamical systems using subspace methods [PDF]

A new procedure to predict with subspace methods is presented in this paper. It is based on combining multiple forecasts obtained from setting a range of values for a specic parameter that is typically xed by the user in the subspace methods literature ...
Alfredo García-Hiernaux
core  

Multi-Criterion Mode Selection in Stochastic Subspace Identification (SSI): Enhancing Reliability in Noisy Environments

Buildings
In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC).
Gürhan Tokgöz, Eda Avanoğlu Sıcacık
doaj   +1 more source

Simulator‐Based Bayesian Inference of Enhanced Geothermal Reservoir Properties

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Inversions of dynamic multi‐scale multi‐physics subsurface system properties rely on the effective sampling of high‐dimensional parameter spaces and are thus notoriously difficult to perform. The associated models are defined by complex nonlinear relationships with potentially stochastic components, which lead to analytically intractable ...
Kwabena Atobra, Gregor Hillers, Vladimir Lyakhovsky, Eyal Shalev, Arto Klami, Ulpu Remes   +5 more
wiley   +1 more source

Dimensionality Reduction Techniques for Analyzing Tsunami Simulations in Hazard Assessment and Forecasting Applications

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract A wide selection of linear and non‐linear dimensionality reduction techniques is evaluated on synthetic tsunami data that is the basis for tsunami hazard assessment and computational forecasting. The data were computed from earthquake rupture forecasts (ERFs) supplying initial generation conditions and a linear long‐wave Green's function ...
Eric L. Geist, Tom Parsons
wiley   +1 more source

Capturing Non‐Linear Neighborhood Structure: An Autoencoder Approach to Census‐Based Dimensionality Reduction for Residential Differentiation

Geographical Analysis, Volume 58, Issue 4, October 2026.
ABSTRACT Dimensionality reduction is fundamental to applied spatial data analysis, condensing high‐dimensional indicators into parsimonious representations for mapping and modeling. Principal component analysis (PCA) remains a dominant approach, yet its linearity assumptions constrain its capacity to capture the complex, non‐linear dependencies ...
Alex Singleton, Owen Goodwin, Stef De Sabbata   +2 more
wiley   +1 more source

Solving Stochastic Climate‐Economy Models: A Deep Least‐Squares Monte Carlo Approach

Mathematical Finance, Volume 36, Issue 4, Page 751-770, October 2026.
ABSTRACT Stochastic versions of recursive integrated climate‐economy assessment models are essential for studying and quantifying policy decisions under uncertainty. However, as the number of state variables and stochastic shocks increases, solving these models via deterministic grid‐based dynamic programming (e.g., value‐function iteration/projection ...
Aleksandar Arandjelović, Pavel V. Shevchenko, Tomoko Matsui, Daisuke Murakami, Tor A. Myrvoll   +4 more
wiley   +1 more source

Realization of stochastic systems with exogenous inputs and subspace system identification methods

, 1999
A stochastic realization theory for a discrete-time stationary process with an ex- ogenous input is developed by extending the classical CCA technique. Some stochastic subspace identification methods are derived by adapting the realization procedure to ...
Tohru Katayama, Giorgio Picci
core