This project aims at focusing on the development novel methods that accounts for non-traditional training objectives (other than mean square prediction error) and corrupted data sequence. This project is expected to result in faster and more accurate training solutions (classification, parameter estimation, short time prediction, tracking) than the currently available ones. The methods developed are application free and concentrates on the triplet of interpretability, robustness and network optimization via deeplearners (DNN).
Doctoral Student at Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Senior Lecturer at Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Professor at Chalmers, Electrical Engineering, Systems and control, Automatic Control
Funding Chalmers participation during 2020–2025
Areas of Advance