What is he best known for?
The fields of study he is best known for:
- Statistics
- Control theory
- Mathematical analysis
Håkan Hjalmarsson focuses on Control theory, System identification, Control engineering, Mathematical optimization and Identification.
His research combines Iterative method and Control theory.
His studies deal with areas such as Estimation theory, Optimization problem, Algorithm, Applied mathematics and Nonlinear system as well as System identification.
His Algorithm research is multidisciplinary, relying on both Basis function, Fuzzy set, Estimator and Function approximation.
His Control engineering research incorporates elements of Control and Control algorithm.
Håkan Hjalmarsson interconnects Design of experiments, Covariance matrix and Noise in the investigation of issues within Mathematical optimization.
His most cited work include:
- Nonlinear black-box modeling in system identification: a unified overview (1638 citations)
- Iterative feedback tuning: theory and applications (711 citations)
- From experiment design to closed-loop control (402 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in System identification, Control theory, Mathematical optimization, Algorithm and Identification.
His System identification research incorporates themes from Linear system, Kernel, Impulse response, Covariance matrix and Applied mathematics.
His Control theory study combines topics from a wide range of disciplines, such as Control engineering and Design of experiments.
His Control engineering study combines topics in areas such as Input design and Control.
His research in Mathematical optimization intersects with topics in Convergence and Norm.
His Algorithm research includes elements of Marginal likelihood and Bayes' theorem.
He most often published in these fields:
- System identification (35.77%)
- Control theory (35.51%)
- Mathematical optimization (23.50%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (21.15%)
- System identification (35.77%)
- Applied mathematics (20.63%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Algorithm, System identification, Applied mathematics, Identification and Mathematical optimization.
When carried out as part of a general Algorithm research project, his work on Hyperparameter is frequently linked to work in Gaussian process, therefore connecting diverse disciplines of study.
His biological study spans a wide range of topics, including Statistics, Linear system, Control theory and Delta method.
His Applied mathematics study integrates concerns from other disciplines, such as Parametric statistics, Parametric model, Estimator, Nonlinear system and Likelihood function.
His Identification research also works with subjects such as
- Function together with Parametrization and Gaussian noise,
- Design of experiments, which have a strong connection to Interconnection, Spectrum and Control engineering.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Kernel and Robustness.
Between 2015 and 2021, his most popular works were:
- Robust EM kernel-based methods for linear system identification (31 citations)
- Application-Oriented Input Design in System Identification: Optimal Input Design for Control [Applications of Control] (27 citations)
- The Box-Jenkins Steiglitz-McBride algorithm (18 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Mathematical analysis
Håkan Hjalmarsson mainly investigates System identification, Mathematical optimization, Algorithm, Identification and Applied mathematics.
His System identification study incorporates themes from Input design, Control theory, Real-time Control System, Control and Noise.
His work in Control theory is not limited to one particular discipline; it also encompasses Model predictive control.
His Mathematical optimization research integrates issues from Marginal likelihood, Kernel and Linear dynamical system.
Many of his research projects under Algorithm are closely connected to Gaussian process and Box–Jenkins with Gaussian process and Box–Jenkins, tying the diverse disciplines of science together.
The study incorporates disciplines such as Design of experiments, Control theory and Interconnection in addition to Identification.
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