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  • 1.
    Yasini, Sholeh
    et al.
    Electrical Engineering Department, Ferdowsi University of Mashhad, Iran.
    Karimpour, Ali
    Naghibi Sistani, Mohammad Bagher
    Data-based Reinforcement Learning algorithm with Experience Replay for Solving Constrained Nonzero-sum Differential Games2014Conference paper (Refereed)
  • 2.
    Yasini, Sholeh
    et al.
    Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
    Karimpour, Ali
    Naghibi Sistani, Mohammad Bagher
    Modares, Hamidreza
    Online concurrent reinforcement learning algorithm to solve two-player zero-sum games for partially unknown nonlinear continuous-time systems2015In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 29, no 4, p. 473-493Article in journal (Refereed)
  • 3.
    Yasini, Sholeh
    et al.
    Department of Electrical Engineering, Ferdowsi University of Mashhad, Iran.
    Naghibi Sistani, Mohammad Bagher
    Karimpour, Ali
    Approximate Dynamic Programming for Two-player Zero-sum Game Related to H∞ Control of Unknown Nonlinear Continuous-time Systems2015In: International Journal of Control, Automation and Systems, ISSN 1598-6446, E-ISSN 2005-4092, Vol. 13, no 1, p. 99-109Article in journal (Refereed)
  • 4.
    Yasini, Sholeh
    et al.
    Electrical Engineering Department, Ferdowsi University of Mashhad.
    Naghibi Sistani, Mohammad Bagher
    Karimpour, Ali
    Policy Iteration Algorithm Based on Experience Replay to Solve H∞ Control Problem of Partially Unknown Nonlinear Systems2014Conference paper (Refereed)
  • 5.
    Yasini, Sholeh
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    High-dimensional online adaptive filtering2017Conference paper (Refereed)
  • 6.
    Yasini, Sholeh
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Worst-Case Prediction Performance Analysis of the Kalman Filter2018In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, no 6, p. 1768-1775Article in journal (Refereed)
    Abstract [en]

    In this paper, we study the prediction performance of the Kalman filter (KF) in a worst case minimax setting as studied in online machine learning, information, and game theory. The aim is to predict the sequence of observations almost as well as the best reference predictor (comparator) sequence in a comparison class. We prove worst-case bounds on the cumulative squared prediction errors using a priori knowledge about the complexity of reference predictor sequence. In fact, the performance of the KF is derived as a function of the performance of the best reference predictor and the total amount of drift that occurs in the schedule of the best comparator.

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