We illustrate several adaptive algorithms for the solutions of bi-infinite singular linear systems. Such algorithms are realized from exact iterative schemes (e.g., Richardson, steepest-descent methods, matching pursuit) by finite dimensional approximations of each iteration, performed with a greedy approach. We show that these algorithms are convergent and optimal with respect to certain spars...

This paper investigates the impact of fast parameter identification methods, which do not require any forward simulations, on model-based glucose control, using retrospective data in the Christchurch Hospital Intensive Care Unit. The integral-based identification method has been previously clinically validated and extensively applied in a number of biomedical applications; and is a crucial elem...

In this paper, a Q-learning algorithm is proposed to solve the linear quadratic regulator problem of black box systems. The only has access input and output measurements. A Luenberger observer parametrization constructed using control new obtained from factorization utility function. An integral reinforcement learning approach used develop approximator structure. gradient descent update rule es...

One of the most striking evidences information loss paradox is that, according to Hawking's calculation, correlation functions a test scalar field exponentially decay in time. In this paper, I argue that judicious use steepest descent expansion on classical saddle point (the Black Hole background), enough change early time into late growing, agreement with retrieval. will explicitly show Jackiw...

This paper presents a data-driven adaptive steady state-integral-derivative (SS-ID) control algorithm that uses gradient descent and recursive least squares (RLS) with forgetting factor. A simplified first-order differential equation of the system was designed its parameters were estimated in real-time using RLS algorithm. The steady-state input for target-state tracking derived based on perfor...