Inference of regulatory relations in dynamical systems is a promising active research
area. Recently, most of the investigations in this field have been stimulated by the
researches in functional genomics. In this thesis, the inferential modeling problem for
switching hybrid systems is studied. The hybrid systems refers to dynamical systems
in which discrete and continuous variables regulate each other, in other words the
jumps and flows are interrelated. In this study, piecewise linear approximations are
used for modeling purposes and it is shown that piecewise linear models are capable
of displaying the evolutionary characteristics of switching hybrid systems approxi-
mately. For the mentioned systems, detection of switching instances and inference of
locally linear parameters from empirical data provides a solid understanding about
the system dynamics. Thus, the inference methodology is based on these issues. The
primary difference of the inference algorithm is the idea of transforming the switch-
ing detection problem into a jump detection problem by derivative estimation from
discrete data. The jump detection problem has been studied extensively in signal
processing literature. So, related techniques in the literature has been analyzed care-
fully and suitable ones adopted in this thesis. The primary advantage of proposed
method would be its robustness in switching detection and derivative estimation. The
theoretical background of this robustness claim and the importance of robustness for
real world applications are explained in detail.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12608789/index.pdf |
| Date | 01 September 2007 |
| Creators | Selcuk, Ahmet Melih |
| Contributors | Oktem, Hakan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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