Date of Award
Doctor of Philosophy (PhD)
Christopher V. Hollot
image processing, measurement selection, model validation, noise compensation, system itentification, wavelet transforms
The aim of this research was to develop a method of system identification that would draw inspiration from the approach taken by human experts for simulation model tuning and validation. Human experts are able to utilize their natural pattern recognition ability to identify the various shape attributes, or signatures, of a time series from simulation model outputs. They can also intelligently and effectively perform tasks ranging from system identification to model validation. However, the feature extraction approach employed by them cannot be readily automated due to the difficulty in measuring shape attributes. In order to bridge the gap between the approach taken by human experts and those employed for traditional iterative approaches, a method to quantify the shape attributes was devised.
The method presented in this dissertation, the Parameter Signature Isolation Method (PARSIM), uses continuous wavelet transformation to characterize specific aspects of the time series shape through surfaces in the time-scale domain. A salient characteristic of these surfaces is their enhanced delineation of the model outputs and/or their sensitivities. One benefit of this enhanced delineation is the capacity to isolate regions of the time-scale plane, coined as parameter signatures, wherein individual output sensitivities dominate all the others. The parameter signatures enable the estimation of each model parameter error separately with applicability to parameter estimation. The proposed parameter estimation method has unique features, one of them being the capacity for noise suppression, wherein the feature of relying entirely on the time-scale domain for parameter estimation offers direct noise compensation in this domain. Yet another utility of parameter signatures is in measurement selection, whereby the existence of parameter signatures is attributed to the identifiability of model parameters through various outputs.
The effectiveness of PARSIM is demonstrated through an array of theoretical models, such as the Lorenz System and the Van der Pol oscillator, as well as through the real-world simulation models of an injection molding process and a jet engine.
McCusker, James Richard, "The Parameter Signature Isolation Method and Applications" (2011). Open Access Dissertations. Paper 381.