Results 271 to 280 of about 1,828,697 (335)

Nonlinear System Identification With Robust Multiple Model Approach

IEEE Transactions on Control Systems Technology, 2020
This brief develops a robust multiple model strategy for nonlinear system identification with system output data corrupted by outliers. The nonlinear system is described as a global model that combines multiple local nonlinear state-space models (SSMs ...
Xin Liu, Xianqiang Yang, Shen Yin
semanticscholar   +1 more source

Set membership identification of nonlinear systems

Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
MILANESE, Mario, NOVARA, Carlo
openaire   +2 more sources

Comparison of nonlinear system identification methods for free decay measurements with application to jointed structures

Journal of Sound and Vibration, 2019
Assembled structures are nonlinear. The sources of this nonlinearity could include the jointed interfaces, damage and wear, non-idealized boundary conditions, or other features inherent in real parts.
M. Jin, M. Brake, Hanwen Song
semanticscholar   +1 more source

Identification of nonlinear system composed of parallel coupling of Wiener and Hammerstein models

Asian journal of control, 2021
In this paper, a new method for the identification of nonlinear system composed by the Wiener and Hammerstein models connected in parallel is presented.
A. Brouri, L. Kadi, K. Lahdachi
semanticscholar   +1 more source

Identification of nonlinear LFR systems with two nonlinearities

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2013
When identifying a system (e.g. mechanical, electrical or chemical) based on inand output measurements and without physical knowledge, an engineer faces many choices. First of all, there exist standard linear models, but when those do not sufficiently well describe the data, nonlinear models should be considered.
Van Mulders, Anne, Vanbeylen, Laurent
openaire   +2 more sources

Characteristic nonlinear system identification: A data-driven approach for local nonlinear attachments

Mechanical systems and signal processing, 2019
This research introduces the characteristic nonlinear system identification (CNSI) procedure for identifying the dynamics of local nonlinear attachments.
K. Moore
semanticscholar   +1 more source

On Using Gated Recurrent Units for Nonlinear System Identification

European Control Conference, 2019
During recent years Deep Learning (DL) methods facilitated impressive progress on various fields of research: Deep Convolutional Neural Networks (CNN) enabled object classification with to this day unmatched precision, while state of the art results in ...
Alexander Rehmer, A. Kroll
semanticscholar   +1 more source

Block-oriented Nonlinear System Identification

2010
Block-oriented Nonlinear System Identification deals with an area of research that has been very active since the turn of the millennium. The book makes a pedagogical and cohesive presentation of the methods developed in that time. These include: * iterative and over-parameterization techniques; * stochastic and frequency approaches; * support-vector ...
Giri, Fouad, Bai, Er-Wei
openaire   +2 more sources

Nonlinear structure system identification

Adaptive Structures Forum, 1996
A new methodology has been developed to identify the change of the local elements of the multi-degree-of freedom structures whose original states are linear by comparing the behaviors of the structural system before and after the change in the frequency domain. These changes of the local elements include that their linear stiffness are reduced or their
Lai-Ah Wong, Jay-Chung Chen
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Deep Recurrent Neural Networks for Nonlinear System Identification

IEEE Symposium Series on Computational Intelligence, 2019
Deep recurrent neural networks are used as a means for nonlinear system identification. It is shown that state space models can be transformed into recurrent neural networks and vice versa. This transformation and the understanding of the long short-term
Max Schüssler, T. Münker, O. Nelles
semanticscholar   +1 more source