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pro vyhledávání: '"Naeem, Muhammad Abdullah"'
Performance of ordinary least squares(OLS) method for the \emph{estimation of high dimensional stable state transition matrix} $A$(i.e., spectral radius $\rho(A)<1$) from a single noisy observed trajectory of the linear time invariant(LTI)\footnote{L
Externí odkaz:
http://arxiv.org/abs/2312.05794
High dimensional random dynamical systems are ubiquitous, including -- but not limited to -- cyber-physical systems, daily return on different stocks of S&P 1500 and velocity profile of interacting particle systems around McKeanVlasov limit. Mathemat
Externí odkaz:
http://arxiv.org/abs/2310.10523
We study the problem of identification of linear dynamical system from a single trajectory, via excitations of isotropic Gaussian. In stark contrast with previously reported results, Ordinary Least Squares (OLS) estimator for even \emph{stable} dynam
Externí odkaz:
http://arxiv.org/abs/2305.12083
Autor:
Naeem, Muhammad Abdullah
In this work, we study non-asymptotic bounds on correlation between two time realizations of stable linear systems with isotropic Gaussian noise. Consequently, via sampling from a sub-trajectory and using \emph{Talagrands'} inequality, we show that e
Externí odkaz:
http://arxiv.org/abs/2304.01708
Via operator theoretic methods, we formalize the concentration phenomenon for a given observable `$r$' of a discrete time Markov chain with `$\mu_{\pi}$' as invariant ergodic measure, possibly having support on an unbounded state space. The main cont
Externí odkaz:
http://arxiv.org/abs/2212.03670
We study the concentration phenomenon for discrete-time random dynamical systems with an unbounded state space. We develop a heuristic approach towards obtaining exponential concentration inequalities for dynamical systems using an entirely functiona
Externí odkaz:
http://arxiv.org/abs/2205.12448
In this work, we show existence of invariant ergodic measure for switched linear dynamical systems (SLDSs) under a norm-stability assumption of system dynamics in some unbounded subset of $\mathbb{R}^{n}$. Consequently, given a stationary Markov cont
Externí odkaz:
http://arxiv.org/abs/2006.08105
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