What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
Lennart Ljung mainly focuses on System identification, Mathematical optimization, Applied mathematics, Identification and Control theory.
His System identification research includes elements of Linear system, Frequency domain, Artificial intelligence, Nonlinear system and Algorithm.
Lennart Ljung works mostly in the field of Algorithm, limiting it down to topics relating to Errors-in-variables models and, in certain cases, Set, as a part of the same area of interest.
His research in Mathematical optimization focuses on subjects like Regularization, which are connected to Finite impulse response.
He combines subjects such as Transfer function, Random variable, Convergence, Estimation theory and Consistency with his study of Applied mathematics.
His Identification study combines topics from a wide range of disciplines, such as Subspace topology and Machine learning, Identifiability.
His most cited work include:
- System Identification: Theory for the User (15753 citations)
- Theory and Practice of Recursive Identification (2665 citations)
- Nonlinear black-box modeling in system identification: a unified overview (1638 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in System identification, Mathematical optimization, Identification, Control theory and Applied mathematics.
Lennart Ljung focuses mostly in the field of System identification, narrowing it down to matters related to Algorithm and, in some cases, Subspace topology.
His research investigates the connection with Mathematical optimization and areas like Regularization which intersect with concerns in Impulse response.
His Identification research incorporates elements of Control engineering, Machine learning, Identifiability and Artificial intelligence.
His study in the field of Adaptive control, Closed loop and LTI system theory is also linked to topics like Mean squared prediction error.
The Applied mathematics study combines topics in areas such as Transfer function, Linear system, Matrix and Estimator, Statistics.
He most often published in these fields:
- System identification (30.96%)
- Mathematical optimization (27.56%)
- Identification (25.33%)
What were the highlights of his more recent work (between 2011-2021)?
- System identification (30.96%)
- Mathematical optimization (27.56%)
- Applied mathematics (21.48%)
In recent papers he was focusing on the following fields of study:
Lennart Ljung mostly deals with System identification, Mathematical optimization, Applied mathematics, Regularization and Algorithm.
His research on System identification concerns the broader Identification.
His research integrates issues of Maximum likelihood, Linear system and Bayesian probability in his study of Mathematical optimization.
His Linear system research is multidisciplinary, incorporating elements of Nonparametric statistics and Noise.
His Algorithm study integrates concerns from other disciplines, such as Dynamical systems theory, Dynamic network analysis and Variable kernel density estimation.
In his research on the topic of Nonlinear system, Range is strongly related with Nonlinear system identification.
Between 2011 and 2021, his most popular works were:
- Survey Kernel methods in system identification, machine learning and function estimation: A survey (370 citations)
- On the estimation of transfer functions, regularizations and Gaussian processes-Revisited (281 citations)
- Identification of Hammerstein-Wiener models (159 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Lennart Ljung mainly investigates Mathematical optimization, System identification, Regularization, Identification and Applied mathematics.
Lennart Ljung interconnects Marginal likelihood, Bayesian probability, Kernel embedding of distributions and Linear regression in the investigation of issues within Mathematical optimization.
His System identification research integrates issues from Transfer function, Empirical Bayes method, Linear system, Kernel method and Algorithm.
His Linear system research focuses on Nonparametric statistics and how it connects with Matrix norm.
His Algorithm study combines topics in areas such as Control theory and Variable kernel density estimation.
His studies in Identification integrate themes in fields like Maximum likelihood, Theoretical computer science and Nonlinear system.
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