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- Robert Babuska

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Engineering and Technology
H-index
62
Citations
18,835
336
World Ranking
555
National Ranking
15

- Artificial intelligence
- Machine learning
- Control theory

Fuzzy logic, Artificial intelligence, Control theory, Fuzzy control system and Reinforcement learning are his primary areas of study. Robert Babuska focuses mostly in the field of Fuzzy logic, narrowing it down to matters related to Nonlinear system and, in some cases, Temperature control. His Artificial intelligence research includes themes of Machine learning and Approximation algorithm.

Robert Babuska has included themes like Estimation theory, Linear model and Model predictive control in his Control theory study. His work in Fuzzy control system addresses subjects such as Mathematical optimization, which are connected to disciplines such as Linearization, Reduction and Repeated game. His biological study spans a wide range of topics, including Variety, Multi-agent system, Markov decision process and State.

- A Comprehensive Survey of Multiagent Reinforcement Learning (1182 citations)
- Fuzzy Modeling for Control (913 citations)
- Reinforcement Learning and Dynamic Programming Using Function Approximators (658 citations)

Robert Babuska mainly focuses on Control theory, Fuzzy logic, Artificial intelligence, Mathematical optimization and Reinforcement learning. His Control theory research incorporates elements of Control engineering and Model predictive control. His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision.

His Mathematical optimization study combines topics from a wide range of disciplines, such as Function and Algorithm. The Reinforcement learning study combines topics in areas such as Markov decision process, Optimal control and Bellman equation. In his research on the topic of Neuro-fuzzy, Defuzzification and Fuzzy classification is strongly related with Fuzzy set operations.

- Control theory (35.97%)
- Fuzzy logic (30.87%)
- Artificial intelligence (30.61%)

- Artificial intelligence (30.61%)
- Reinforcement learning (24.74%)
- Robot (14.80%)

Robert Babuska mainly investigates Artificial intelligence, Reinforcement learning, Robot, Control theory and Symbolic regression. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Computer vision. His Reinforcement learning research includes themes of Mathematical optimization, Bellman equation, State, Control and Multi-agent system.

His Robot kinematics study in the realm of Robot connects with subjects such as Gait. Control theory is frequently linked to Control engineering in his study. The various areas that Robert Babuska examines in his Symbolic regression study include Algorithm, Nonlinear autoregressive exogenous model, State space and Local regression.

- Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning (135 citations)
- Deep convolutional neural networks for detection of rail surface defects (123 citations)
- Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks (112 citations)

- Artificial intelligence
- Machine learning
- Statistics

His primary areas of study are Reinforcement learning, Artificial intelligence, Control theory, Robot and Robotics. His Reinforcement learning study incorporates themes from Control, Multi-agent system, State and Thermostat. His Control research includes elements of Stability and Setpoint.

His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning. His work carried out in the field of Robot brings together such families of science as Approximate solution, Inverted pendulum and Hybrid automaton. His Robotics study combines topics in areas such as Lifelong learning, Model predictive control and Trajectory.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Fuzzy Modeling for Control

Robert Babuska.

**(1998)**

1974 Citations

A Comprehensive Survey of Multiagent Reinforcement Learning

L. Busoniu;R. Babuska;B. De Schutter.

systems man and cybernetics **(2008)**

1626 Citations

Reinforcement Learning and Dynamic Programming Using Function Approximators

Lucian Busoniu;Robert Babuska;Bart De Schutter;Damien Ernst.

**(2010)**

991 Citations

Similarity measures in fuzzy rule base simplification

M. Setnes;R. Babuska;U. Kaymak;H.R. van Nauta Lemke.

systems man and cybernetics **(1998)**

647 Citations

A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients

I. Grondman;L. Busoniu;G. A. D. Lopes;R. Babuska.

systems man and cybernetics **(2012)**

519 Citations

Perspectives of fuzzy systems and control

Antonio Sala;Thierry Marie Guerra;Robert Babuška.

Fuzzy Sets and Systems **(2005)**

477 Citations

An overview of fuzzy modeling for control

R. Babuška;H.B. Verbruggen.

Control Engineering Practice **(1996)**

388 Citations

Neuro-fuzzy methods for nonlinear system identification

Robert Babuska;Henk B. Verbruggen.

Annual Reviews in Control **(2003)**

384 Citations

Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models

J. Abonyi;R. Babuska;F. Szeifert.

systems man and cybernetics **(2002)**

378 Citations

Multi-agent Reinforcement Learning: An Overview

Lucian Buşoniu;Robert Babuška;Bart De Schutter.

**(2010)**

367 Citations

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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