Machine Learning Techniques for Large-Scale System Modeling
Date
2011-08-31
Authors
Lv, Jiaqing
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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.
Description
Keywords
nonparametric estimation, semiparametric, MISO Hammerstein model, curse of dimensionality, model selection, Lasso, transient stability boundary, machine learning