Browsing by Subject "Steady-state analysis"

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    Decompositional analysis of Kronecker structured Markov chains
    (Kent State University, 2008) Bao, Y.; Bozkur, I. N.; Dayar, T.; Sun, X.; Trivedi, K. S.
    This contribution proposes a decompositional iterative method with low memory requirements for the steadystate analysis ofKronecker structured Markov chains. The Markovian system is formed by a composition of subsystems using the Kronecker sum operator for local transitions and the Kronecker product operator for synchronized transitions. Even though the interactions among subsystems, which are captured by synchronized transitions, need not be weak, numerical experiments indicate that the solver benefits considerably from weak interactions among subsystems, and is to be recommended specifically in this case. © 2008, Kent State University.
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    The Krylov-proportionate normalized least mean fourth approach: formulation and performance analysis
    (Elsevier BV, 2015) Sayin, M. O.; Yilmaz, Y.; Demir, A.; Kozat, S. S.
    We propose novel adaptive filtering algorithms based on the mean-fourth error objective while providing further improvements on the convergence performance through proportionate update. We exploit the sparsity of the system in the mean-fourth error framework through the proportionate normalized least mean fourth (PNLMF) algorithm. In order to broaden the applicability of the PNLMF algorithm to dispersive (non-sparse) systems, we introduce the Krylov-proportionate normalized least mean fourth (KPNLMF) algorithm using the Krylov subspace projection technique. We propose the Krylov-proportionate normalized least mean mixed norm (KPNLMMN) algorithm combining the mean-square and mean-fourth error objectives in order to enhance the performance of the constituent filters. Additionally, we propose the stable-PNLMF and stable-KPNLMF algorithms overcoming the stability issues induced due to the usage of the mean fourth error framework. Finally, we provide a complete performance analysis, i.e.; the transient and the steady-state analyses, for the proportionate update based algorithms, e.g.; the PNLMF, the KPNLMF algorithms and their variants; and analyze their tracking performance in a non-stationary environment. Through the numerical examples, we demonstrate the match of the theoretical and ensemble averaged results and show the superior performance of the introduced algorithms in different scenarios.
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    On the distribution of throughput of transfer lines
    (Palgrave Macmillan, 2000) Dinçer, C.; Deler, B.
    Transfer lines simply characterise the interrelationship of manufacturing stages with their buffers and they are used to model the key features of such manufacturing environments with simplifying assumptions. There is a vast literature on these systems, however, little has been done on the transient analysis of the transfer lines by making use of the higher order moments of their performance measures due to the difficulty in determining the evolution of the stochastic processes under consideration. This paper examines the transient behaviour of relatively short transfer lines and derives the distribution of the performance measures of interest. An experiment is designed in order to compare the results of this study with the state-space representations and the simulation. Furthermore, extensions are briefly discussed and directions for future research are suggested.
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    Performance analysis of scalar diffusion strategy over distributed network
    (IEEE, 2014) Sayın, Muhammed Ö.; Kozat, Süleyman Serdar
    In this paper, we present a complete performance analysis of the scalar diffusion strategies over distributed networks. Scalar diffusion strategies are based on the diffusion implementation and adaptive extraction of the information from the diffusion data which is compressed into a scalar. This strategy require significantly less communication load while achieving similar performance with the full information exchange configuration. Here, we provide the transient and steady-state analysis of the scalar diffusion strategies for Gaussian regressors. Finally, in the numerical examples, we demonstrate that the theoretical results match with the simulation results.