Browsing by Subject "linear programming"

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    Mathematical models of evolution
    (1992) Özaktaş, Hakan
    Two categories of evolutionary models are analyzed. The first category is the so-called autogenesis phenomenon. The emergence of self-organization, which has been discussed previously by Csanyi and Kampis is verified. The model is extended to an interrelated multi-level autogenesis system. Similarly, self-organization is observed in a hierarchical order for ea.ch level. The second category is the optimization model ol evolution. An ongoing process of consecutive LP runs associated with random perturbation of the parameters at each step, is designed to simulate the evolutionary mechanisms (mutations, variations and selection) and the population dynamics of a hypothetical ecological system. Two diihu'ent LP ajrproaches for Lotka-Volterra systems are compared and contrastc'd. A brief history of evolution a.nd some mathematical models that have been constructed up to date are also descrilred in the beginning chapter.
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    Noise benefits in joint detection and estimation systems = Birlikte sezim ve kestirim sistemlerinde gürültünün faydaları
    (2014) Akbay, Abdullah Başar
    Adding noise to inputs of some suboptimal detectors or estimators can improve their performance under certain conditions. In the literature, noise benefits have been studied for detection and estimation systems separately. In this thesis, noise benefits are investigated for joint detection and estimation systems. The analysis is performed under the Neyman-Pearson (NP) and Bayesian detection frameworks and the Bayesian estimation framework. The maximization of the system performance is formulated as an optimization problem. The optimal additive noise is shown to have a specific form, which is derived under both NP and Bayesian detection frameworks. In addition, the proposed optimization problem is approximated as a linear programming (LP) problem, and conditions under which the performance of the system cannot be improved via additive noise are obtained. With an illustrative numerical example, performance comparison between the noise enhanced system and the original system is presented to support the theoretical analysis.