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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Journal ArticleDOI
Machine Learning Algorithms in Bipedal Robot Control
TL;DR: A review of recent advances on the state-of-the-art learning algorithms and their applications to bipedal robot control is given.
Journal ArticleDOI
Cross-Entropy Optimization of Control Policies With Adaptive Basis Functions
TL;DR: An algorithm for direct search of control policies in continuous-state discrete-action Markov decision processes, which requires vastly fewer BFs than value-function techniques with equidistant BFs, and outperforms policy search with a competing optimization algorithm called DIRECT.
Journal ArticleDOI
Rule base reduction: some comments on the use of orthogonal transforms
M. Setnes,Robert Babuska +1 more
TL;DR: It is shown how detection of redundant rules can be introduced in OLS by a simple extension of the algorithm and discusses the performance of rank-revealing reduction methods and advocate the use of a less complex method based on the pivoted QR decomposition.
Book ChapterDOI
Neuro-fuzzy methods for modeling and identification
TL;DR: This chapter addresses the use of neuro-fuzzy models in system identification, a gray-box technique on the boundary between neural networks and qualitative fuzzy models.
Proceedings ArticleDOI
A new identification method for linguistic fuzzy models
TL;DR: The approach is based on the novel concept of complementary fuzzy partition which is derived from the partition of a fuzzy linear model and combines a well established identification method for fuzzy linear models with a good semantic interpretation capabilities of linguistic fuzzy models.