What is he best known for?
The fields of study he is best known for:
- Statistics
- Control theory
- Artificial intelligence
Michel Verhaegen mainly investigates Control theory, System identification, Subspace topology, Algorithm and Control theory.
Specifically, his work in Control theory is concerned with the study of Nonlinear system.
Michel Verhaegen has researched System identification in several fields, including Linear model, Multivariable calculus, Frequency response, MIMO and Mathematical optimization.
His Subspace topology research is multidisciplinary, incorporating elements of Bilinear interpolation, Affine transformation, Identification, Kernel method and Observability.
His Algorithm study incorporates themes from Linear system, Linear subspace, State space, State-space representation and White noise.
The various areas that Michel Verhaegen examines in his Control theory study include Optimal control, Stability, Linear matrix inequality, Fault tolerance and Fault.
His most cited work include:
- Identification of the deterministic part of MIMO state space models given in innovations form from input-output data (792 citations)
- Subspace model identification Part 1. The output-error state-space model identification class of algorithms (588 citations)
- Filtering and System Identification: A Least Squares Approach (404 citations)
What are the main themes of his work throughout his whole career to date?
Michel Verhaegen mainly investigates Control theory, Algorithm, Optics, Subspace topology and Mathematical optimization.
His biological study spans a wide range of topics, including Control engineering and System identification.
His Algorithm research includes elements of Matrix, Multivariable calculus, State-space representation, Phase retrieval and Kalman filter.
His research in Subspace topology tackles topics such as Identification which are related to areas like State space.
His work carried out in the field of Mathematical optimization brings together such families of science as Computational complexity theory, Applied mathematics and Convex optimization.
The Wavefront study which covers Artificial intelligence that intersects with Pattern recognition.
He most often published in these fields:
- Control theory (36.52%)
- Algorithm (21.48%)
- Optics (18.36%)
What were the highlights of his more recent work (between 2016-2021)?
- Optics (18.36%)
- Algorithm (21.48%)
- Adaptive optics (14.45%)
In recent papers he was focusing on the following fields of study:
Michel Verhaegen mostly deals with Optics, Algorithm, Adaptive optics, Phase retrieval and Wavefront.
His study in Algorithm is interdisciplinary in nature, drawing from both Subspace topology, Matrix, Rank and Identification.
Michel Verhaegen has included themes like Structure, Dynamical systems theory, Matrix decomposition, Markov chain and Realization in his Subspace topology study.
His Markov chain study combines topics in areas such as Markov process, Control theory, Filter, Filter design and Fault.
The study incorporates disciplines such as Optimization problem and Linear system in addition to Identification.
His Wavefront research also works with subjects such as
- Artificial intelligence which intersects with area such as Pattern recognition,
- Spline that intertwine with fields like Nonlinear system.
Between 2016 and 2021, his most popular works were:
- Subspace identification of individual systems in a large-scale heterogeneous network (41 citations)
- Constrained Subspace Method for the Identification of Structured State-Space Models (COSMOS) (32 citations)
- Identification of structured state-space models (26 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Optics
- Artificial intelligence
Algorithm, Optics, Subspace topology, Identification and Control theory are his primary areas of study.
His Algorithm research incorporates elements of Gerchberg–Saxton algorithm, Nonlinear system, Regression and Rank.
His work in Regression tackles topics such as Kriging which are related to areas like System identification.
His Subspace topology research incorporates themes from Structure, Tensor, Parametrization, Markov chain and Realization.
In general Identification, his work in Parameter identification problem is often linked to Heterogeneous network linking many areas of study.
Michel Verhaegen integrates several fields in his works, including Control theory and Inverse.
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