Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 41 for “"non-linear models"”.
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The Low-rank Simplicity Bias in Deep Networks
… that our claim holds true on finite width linear and non-linear models on practical learning paradigms and show that on natural data, these are often the solutions that generalize well. We then show that the simplicity bias exists at both initialization and after training and is resilient …
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Predicting Spatial Variability of Soil Organic Carbon in Delmarva Bays
… assessed: (1) accuracy of topographic-based non-linear models for predicting SOC; and (2) the effect of analytic strategies and soil condition on performance of spectral-based models for predicting SOC. SOC data came from 28 agriculturally converted Delmarva Bays sampled down to 1 meter. R2 …
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Methods of non-linear least squares estimation
… the least squares estimates of the parameters in non-linear statistical models. It is first briefly indicated why non-linear models require iterative numerical estimating procedures, unlike the straightforward algebraic estimation possibly in the linear case. Then the following six general …
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A Statistical Analysis Of Construction Equipment Repair Costs Using Field Data & The Cumulative Cost Model
… four different companies. Nineteen different linear and transformed non-linear models were evaluated. A second-order polynomial expression was selected as the best. It was demonstrated how this expression could be incorporated in the Cumulative Cost Model developed by Vorster where it can be …
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A comparative analysis of non-linear techniques in South African stock selection
… Literature has shown that the classical linear approach to modelling the interactions among company-specific factors and its stock market re- turns in time have become less suited for capturing the movements of the stock market. Hence, attempts to predict the performance of a stock have …
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Time series analysis of ECG data and unreliable forecasting
… (ECG) time series data. We usually use linear models because they are simple and easy to apply. But linear systems that are used to describe complex biological system such as ECG data are no longer satisfactory. For the ECG data , it is expected that a non linear system will provide more …
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Latent trait estimation for item bundle structures
… of the latent space. Factor analytic models seem to be the most suitable class of models available to deal with this problem because they provide a means to account for the specification of the intra-group structure. This investigation describes alternative ways to estimate latent …
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Adaptive Synchronization of Chaos for Secure Communication
… of chaos for secure communication. Several non-linear models were studied, the one-dimensional logistic map, two-dimensional coupled logistic map, three dimensional Lorenz systems with and without time delay. Numerical simulation using MATLAB was conducted. The Lorenz system with time delay …
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Accurate capacitive metrology for atomic force microscopy
… target relative to the capacitance sensors, models for the capacitance between a sphere and a circular disc were developed with the assistance of Katherine Lilienkamp. One of the resulting non-linear models was combined with the appropriate kinematic transformations to accurately perform …
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Hotel occupancy rate volatility and its determinants
… rates. The present study aims to develop models that could predict the turning points of the upward and downward trends in hotel occupancy rates so that hoteliers would know in advance when the current trend would change for the better or worse. These models are developed not for …
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Market Efficiency of African Stock Markets
… analyses. First, most of the methods used were linear in nature although the data generating process of stock market data is nonlinear and hence nonlinear methods maybe more appropriate in its analysis. Also these linear methods tested the efficiency of African markets in absolute form, however, …
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Three essays on the predictive content and predictability of the nominal exchange rates in a changing world
… techniques, taking into account possible non-linearities and time-variations of the Taylor rule relationships, while we also use Bayesian methods and real-time (vintage) data for the variables that suffer from consecutive revisions. Chapter 3 reviews the well-known ‘Meese and Rogoff’ …
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Optimal Control of a Stepping Motor
… equivalent circuits. ‘Exact’ and simplified models for a permanent magnet motor are presented, and simple linear end non-linear models are used to theoretically optimise the input variables. The second part (chapters 5-6) is devoted to the drive circuit and the control of stepping motors. A …
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Unravelling black box machine learning methods using biplots
… and away from older methods such as generalised linear models. However, their use is currently limited because they are seen as “black box” models, which gives predictions without justifications and which are therefore not understood and cannot be trusted. The goal of this dissertation is to …
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Linear and non-linear mechanistic modeling and simulation of the formation of carbon adsorbents
… the current work has formulated a set of linear and non-linear models of varied complexity; derived the governing equations of the models formulated; obtained the analytical solutions of the governing equations whenever possible; simulated one of the models by the Monte Carlo method; and …
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Classification under input uncertainty with support vector machines
… on the target, which can be effective for simple models. However, for more complex models, and where a richer description of anisotropic uncertainty is available, these approaches can suffer. The principal focus of this thesis is the development of advanced classification approaches that can …
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Combining Multivariate Statistical Methods and Spatial Analysis to Characterize Water Quality Conditions in the White River Basin, Indiana, U.S.A.
… and the spatial data is then modeled via linear and non-linear multivariate methods. The linear statistical multivariate method uses a combination of principal component analysis, cluster analysis, and discriminant analysis, whereas the non-linear multivariate method uses a combination of …
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ALTERATIONS OF RIVER FLOW REGIMES IN THE NORTHERN ROCKY MOUNTAINS, USA
… Rocky Mountains over 59 years. I applied linear and non-linear models to the data to determine what, if any, alterations have occurred in the annual flow regimes. By comparing the different results from the natural and anthropogenically-modified river basins, I was able to distinguish the …
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ALTERATIONS OF RIVER FLOW REGIMES IN THE NORTHERN ROCKY MOUNTAINS, USA
… Rocky Mountains over 59 years. I applied linear and non-linear models to the data to determine what, if any, alterations have occurred in the annual flow regimes. By comparing the different results from the natural and anthropogenically-modified river basins, I was able to distinguish the …
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An explorative study of linear vs non-linear hedonic pricing of second-hand cars in South Africa
… Using a hedonic pricing approach to develop four linear and four non-linear regression models the study analyses a comprehensive dataset of 4,386 second-hand vehicles collected from the Cars.co.za website in 2018 and focuses on eight key vehicle attributes, namely age, mileage, region (province of …
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