R
Robert Babuska
Researcher at Delft University of Technology
Publications - 381
Citations - 17611
Robert Babuska is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Fuzzy logic & Reinforcement learning. The author has an hindex of 56, co-authored 371 publications receiving 15388 citations. Previous affiliations of Robert Babuska include Carnegie Mellon University & Czech Technical University in Prague.
Papers
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Book ChapterDOI
Takagi-Sugeno Fuzzy Models
TL;DR: This chapter introduces the continuous-time Takagi-Sugeno (TS) fuzzy systems that are employed throughout the book and presents methods to construct TS models that represent or approximate a nonlinear dynamic system starting from a given model of this system.
Journal ArticleDOI
Matlab design environment for robotic manipulators
TL;DR: An automated modelling and control design environment for serial manipulators has been implemented in Matlab/Simulink and has been used in the design of a control system for a seven-degree-of-freedom manipulator in a tunnel-boring machine.
Proceedings ArticleDOI
An improved multiagent reinforcement learning algorithm
TL;DR: An improved reinforcement learning algorithm is proposed, based on linear programming method for finding the best-response policy, that has some properties, such as easy computation, simple operation procedure and can guarantee a good learning convergence.
Journal ArticleDOI
Comments on the benchmarks in "A proposal for improving the accuracy of Linguistic Modeling" and related articles
TL;DR: It is argued that benchmark examples that are used in articles to demonstrate the effectiveness of fuzzy modeling techniques should be selected with great care and critical analysis of the results should be made and linear models should be regarded as a lower bound on the acceptable performance.
Proceedings ArticleDOI
Structure selection for nonlinear models with mixed discrete and continuous inputs: a comparative study
D. Girimonte,Robert Babuska +1 more
TL;DR: A comparison of two methods for selecting inputs in nonlinear models with mixed discrete (categorical) and continuous variables is presented, showing that the fuzzy clustering-based method performs more consistently in selecting the model structure and is much faster then the wrapper approach.