Sabine Horvath

Papers

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About

Sabine Horvath is a leading researcher in robotics and intelligent control systems, with a primary focus on system identification and dynamic modeling. Her most-cited work, "System identification of a robot arm with extended Kalman filter and artificial neural networks" (2019, 17 citations), represents a significant contribution to the field by developing a hybrid algorithm that combines the Extended Kalman Filter (EKF) with artificial neural networks (ANN). This innovative approach enables non-parametric modeling of complex robotic systems, allowing for accurate prediction of future behavior without requiring detailed physical models. Horvath's research bridges classical estimation theory with modern machine learning, offering practical solutions for real-time control of robot arms. Her work is particularly valuable for applications in industrial automation and adaptive robotics, where precise dynamic characterization is essential. By demonstrating that ANNs can effectively complement traditional filtering techniques, Horvath has opened new pathways for more flexible and robust robotic control systems. Her research continues to influence engineers and researchers working at the intersection of estimation theory, neural networks, and mechatronic system design.