System Identification: A Frequency Domain Approach

By R. Pintelon, Johan Schoukens. 2012

2,117 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e28d1aebca9fcd36d0a23b494b9dfa2ef8795277

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Cooperative Adaptive Cruise Control: Network-Aware Analysis of String Stability (2014, background): “The velocity trajectory for the leader vehicle has been designed as a random phase multisine excitation input [24], [25].”
Intrinsic instability of flame–acoustic coupling (2014, unknown): “To solve this problem, a model based on the frequency response data should be identified first [37].”
Data‐driven model reference control with asymptotically guaranteed stability (2011, background): “The correlation criterion is a continuous function of this variable, which leads to ([28], p.”
Online Grid Impedance Measurement Using Discrete-Interval Binary Sequence Injection (2013, methodology): “The method tends to cancel out the effect of noise (that is uncorrelated with the actual voltages and currents) both from the input and output sides [14].”
Nonlinear System Identification: A User-Oriented Road Map (2019, methodology): “2F excited frequencies (see also Figure S2 for an illustration on a sine excitation with frequencies {-1,1}) [8]: α ∑”
Kernel methods in system identification, machine learning and function estimation: A survey (2014, background): “There is a very extensive literature on system identification , with many text books, like Ljung (1999) and Pintelon and Schoukens (2012a).”
Fast maximum-likelihood identification of modal parameters with uncertainty intervals: A modal model-based formulation (2013, methodology): “(2) used for the ML-MM approach is numerically more stable than the parameterization used for ML-CDM approach, which uses a rational fraction common denominator polynomial model [13].”
A New Scanning Method for Fast Atomic Force Microscopy (2011, unknown): “System identification is an experimental approach to modeling, where mathematical models are obtained from a set of input and output data [27].”