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dc.contributor.supervisor Pawlak, Miroslaw (Electrical & Computer Engineering) en_US
dc.contributor.author Lv, Jiaqing
dc.date.accessioned 2011-08-31T17:49:27Z
dc.date.available 2011-08-31T17:49:27Z
dc.date.issued 2011-08-31
dc.identifier.uri http://hdl.handle.net/1993/4808
dc.description.abstract This thesis is about some issues in system modeling: The first is a parsimonious representation of MISO Hammerstein system, which is by projecting the multivariate linear function into a univariate input function space. This leads to the so-called semiparamtric Hammerstein model, which overcomes the commonly known “Curse of dimensionality” for nonparametric estimation on MISO systems. The second issue discussed in this thesis is orthogonal expansion analysis on a univariate Hammerstein model and hypothesis testing for the structure of the nonlinear subsystem. The generalization of this technique can be used to test the validity for parametric assumptions of the nonlinear function in Hammersteim models. It can also be applied to approximate a general nonlinear function by a certain class of parametric function in the Hammerstein models. These techniques can also be extended to other block-oriented systems, e.g, Wiener systems, with slight modification. The third issue in this thesis is applying machine learning and system modeling techniques to transient stability studies in power engineering. The simultaneous variable section and estimation lead to a substantially reduced complexity and yet possesses a stronger prediction power than techniques known in the power engineering literature so far. en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject nonparametric estimation en_US
dc.subject semiparametric en_US
dc.subject MISO Hammerstein model en_US
dc.subject curse of dimensionality en_US
dc.subject model selection en_US
dc.subject Lasso en_US
dc.subject transient stability boundary en_US
dc.subject machine learning en_US
dc.title Machine Learning Techniques for Large-Scale System Modeling en_US
dc.type info:eu-repo/semantics/masterThesis
dc.type master thesis en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee Yahampath, Pradeepa (Electrical & Computer Engineering) Thavaneswaran, A. (Statistics) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2011 en_US


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