Nonlinear system identification: A user-oriented road map
J Schoukens, L Ljung - IEEE Control Systems Magazine, 2019 - ieeexplore.ieee.org
Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …
Identification of block-oriented nonlinear systems starting from linear approximations: A survey
M Schoukens, K Tiels - Automatica, 2017 - Elsevier
Block-oriented nonlinear models are popular in nonlinear system identification because of
their advantages of being simple to understand and easy to use. Many different identification …
their advantages of being simple to understand and easy to use. Many different identification …
Decoupling multivariate polynomials using first-order information and tensor decompositions
We present a method to decompose a set of multivariate real polynomials into linear
combinations of univariate polynomials in linear forms of the input variables. The method …
combinations of univariate polynomials in linear forms of the input variables. The method …
Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling
Nonlinear state-space modelling is a very powerful black-box modelling approach. However
powerful, the resulting models tend to be complex, described by a large number of …
powerful, the resulting models tend to be complex, described by a large number of …
Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part II)
In the second segment of the tutorial, we transition from the granularity of local
interpretability to a broader exploration of eXplainable AI (XAI) methods. Building on the …
interpretability to a broader exploration of eXplainable AI (XAI) methods. Building on the …
Decoupling multivariate polynomials for nonlinear state-space models
J Decuyper, P Dreesen, J Schoukens… - IEEE Control …, 2019 - ieeexplore.ieee.org
Multivariate polynomials are omnipresent in black-box modelling. They are praised for their
flexibility and ease of manipulation yet typically fall short in terms of insight and …
flexibility and ease of manipulation yet typically fall short in terms of insight and …
Generalized rational functions for reduced-complexity behavioral modeling and digital predistortion of broadband wireless transmitters
In this paper, we present and analyze rational-function-based digital predistortion (DPD) of
transmitters for broadband applications where system noise and prominent memory effects …
transmitters for broadband applications where system noise and prominent memory effects …
Tensor-based two-layer decoupling of multivariate polynomial maps
In this paper, we introduce a new decomposition of multivariate maps that generalizes the
decoupling problem recently proposed in the system identification community. In the context …
decoupling problem recently proposed in the system identification community. In the context …
Parametric identification of parallel Wiener systems
M Schoukens, Y Rolain - IEEE Transactions on Instrumentation …, 2012 - ieeexplore.ieee.org
This paper proposes a parametric identification method for parallel Wiener systems. The
linear dynamic parts of the Wiener system are modeled by a parametric rational function in …
linear dynamic parts of the Wiener system are modeled by a parametric rational function in …
[HTML][HTML] Identification of structured nonlinear state–space models for hysteretic systems using neural network hysteresis operators
K Krikelis, JS Pei, K van Berkel, M Schoukens - Measurement, 2024 - Elsevier
Hysteretic system behavior is ubiquitous in science and engineering fields including
measurement systems and applications. In this paper, we put forth a nonlinear state–space …
measurement systems and applications. In this paper, we put forth a nonlinear state–space …