.png){kind=icon}
.png){kind=icon}
Return
A generalized multiplicative regularization for input estimation
.png){kind=icon}
OA
AI
DOI：10.1016/j.ymssp.2021.107637.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Input estimation remains an important problem for the structural dynamics community as evidenced by the abundant literature dedicated to this topic in the recent years. Generally speaking, inverse methods can be classified into two groups. The first group includes methods that are specifically designed to solve the inverse problem in the time or frequency domains. In the time domain, one can cite Kalman-like approaches [1?3] or dynamic programming [4?6], while, in the frequency domain, methods based on the filtering of the equation of motion of structures, such as beams, cylindrical shells or This paper implements a generalized multiplicative regularization for estimating the mechanical loads acting on a linear structure. The proposed strategy extends the ordinary multiplicative regularization, previously published by the authors, by introducing an extra tuning parameter, which is determined through an original iterative procedure. To assess the practical interest and the overall performances of the proposed approach, numerical and real-world applications are proposed. Obtained results illustrate the influence of the extra tuning parameter according to the measurement noise level and highlight the benefits brought by the generalized multiplicative regularization in terms of solution accuracy. (c) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Linear inverse problem
Force reconstruction
Multiplicative regularization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
1.3W
Citations:
6.6W