I received my M.Sc. and PhD degrees in Control Engineering from Ferdowsi University of Mashhad, Iran. Currently, I am a postdoctoal researcher at the Division of Systems and Control, Department of Information Technology, Uppsala University, working with Professor Torbjörn Wigren. My current research is focused on convergence analysis of networked recursive identification algorithms with output quantization.
Keywords: machine learning system identification signal processing online prediction
Also available at
- Convergence analysis of networked recursive identification algorithms with output quantization.
- Prediction performance analysis of adaptive filtering using a class of prediction methods, called universal prediction.
- High-dimensional adaptive filtering using random projection technique.
S. Yasini and T. Wigren, Convergence Analysis of Adaptive Filtering with Quantized Output by Simulation of Associated ODEs, Technical Report, March 2017, Uppsala University.
S. Yasini and Kristiaan Pelckmans, High-dimensional online adaptive filtering. ERNSI Workshop, September 25-28 2016 (Poster Presentation).
S. Yasini and K. Pelckmans, Minimax performance analysis of the Kalman filter. Reglermöte 2016, Gothenburg, 8-9 June 2016 (Oral Presentation). http://easychair.org/smart-program/RM16/2016-06-09.html#talk:23229
S. Yasini and T. Wigren, A Necessary Condition for Parametric Convergence in Recursive Networked Identification. In preparation.
S. Yasini and T. Wigren, Convergence Analysis of a Networked Recursive Identification Algorithm with Output Quantization by Simulation of Associated ODEs. submitted to CDC2017.
S. Yasini and Kristiaan Pelckmans, Worst-case Prediction Performance of the Kalman Filter. http://arxiv.org/abs/1611.02050.
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