What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Electrical engineering
His primary areas of investigation include Artificial neural network, Mathematical optimization, Nonlinear system, Algorithm and System identification.
His research investigates the link between Artificial neural network and topics such as Minification that cross with problems in Parametric statistics.
His Mathematical optimization research includes elements of Control engineering and Energy management.
His Nonlinear system research integrates issues from Consistency and System dynamics.
His work on Estimation theory as part of general Algorithm study is frequently linked to Hyperplane, therefore connecting diverse disciplines of science.
His System identification study improves the overall literature in Identification.
His most cited work include:
- Nonlinear black-box modeling in system identification: a unified overview (1638 citations)
- Nonlinear black-box models in system identification: mathematical foundations (357 citations)
- Component sizing of a plug-in hybrid electric powertrain via convex optimization (154 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Nonlinear system, Control theory, Mathematical optimization, Algorithm and Artificial neural network.
His work carried out in the field of Nonlinear system brings together such families of science as Nonlinear system identification, System identification, Identification and State space.
In his research, Black box is intimately related to Parameter identification problem, which falls under the overarching field of System identification.
He has researched Mathematical optimization in several fields, including Estimation theory and Parametric statistics.
His work on Adaptive filter as part of general Algorithm research is frequently linked to Hyperplane, thereby connecting diverse disciplines of science.
His Artificial neural network study deals with the bigger picture of Artificial intelligence.
He most often published in these fields:
- Nonlinear system (25.47%)
- Control theory (21.74%)
- Mathematical optimization (18.01%)
What were the highlights of his more recent work (between 2016-2020)?
- Artificial intelligence (12.42%)
- Reinforcement learning (3.73%)
- Model predictive control (8.70%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Reinforcement learning, Model predictive control, Control theory and Control theory are his primary areas of study.
Jonas Sjöberg combines subjects such as Machine learning and Identification with his study of Artificial intelligence.
In general Machine learning, his work in Artificial neural network is often linked to Uncertainty estimation linking many areas of study.
The Identification study which covers Simple linear regression that intersects with Nonlinear system.
In his work, Jonas Sjöberg performs multidisciplinary research in Nonlinear system and Numerical integration.
His studies deal with areas such as Algorithm and Collision avoidance as well as Intersection.
Between 2016 and 2020, his most popular works were:
- Model-Based Lithium-Ion Battery Resistance Estimation From Electric Vehicle Operating Data (32 citations)
- Traffic Coordination at Road Intersections: Autonomous Decision-Making Algorithms Using Model-Based Heuristics (29 citations)
- A Two-Level MPC for Energy Management Including Velocity Control of Hybrid Electric Vehicles (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Electrical engineering
Jonas Sjöberg mainly investigates Algorithm, Parameter identification problem, Model predictive control, System identification and Optimal control.
His biological study spans a wide range of topics, including Intersection, Q-learning, Trajectory and Nonlinear system.
His Nonlinear system study combines topics from a wide range of disciplines, such as Black box, Identification, White box, Benchmark and Flexibility.
Jonas Sjöberg has included themes like Powertrain, Control theory, Maximum principle and Reinforcement learning in his Model predictive control study.
His System identification research includes themes of Linear model, Frequency domain, Identifiability and Applied mathematics.
His research in Optimal control intersects with topics in Intelligent agent, Simulation and Heuristics.
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