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Hierarchical identification of large-scale system models

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dc.contributor.author Jankovi'c B en
dc.date.accessioned 2016-09-22T06:30:09Z
dc.date.available 2016-09-22T06:30:09Z
dc.date.submitted 1997 en
dc.identifier.uri http://hdl.handle.net/20.500.11892/4838
dc.description.abstract One of the main challenges in system theory is identification of dynamic systems. Identification is a process of creating mathematical models of physical systems from input-output observations. One of the problems associated with identification methods is that they're in general, numerically very intensive. A conceptually new method of hierarchical identification developed in this thesis is aimed at speeding up identification of the large-scale system models. This is achieved by using "divide and conquer" approach and parallel processing techniques. The hierarchical identification method is a two phase method resulting in a high-order composite model. Composite models are chosen in such a way that its subsystems can be determined independently and in parallel. This is done in phase 1. In phase 2 parameters of subsystem interactions are determined. The method was shown to reduce numerical effort required for identification of both single-input, single-output and multiple-input, multiple-output systems en
dc.language English en
dc.subject Electrical and Electronic engineering en
dc.title Hierarchical identification of large-scale system models en
dc.type Doctoral degree en
dc.description.degree DTech en

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