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 12 of 12 for “"Sensitivity to Initial Conditions"”.
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Overcoming unrealistic behavior of landscape evolution models attributed to the stream power incision model: Scale invariance and ultra-sensitivity to initial conditions
… evolution models (LEMs) have been used as tools for geomorphologist to explain and understand mountain formation and landform development. Initially these models were conceptual ideas of how geomorphic processes create the landscapes we see today. Now, we predominantly use numerical models …
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Nonlinear Adaptive Estimation Andits Application To Synchronization Of Lorenz System
… known apriori. An adaptive algorithm is proposed to estimate both the state and system parameters. Since Lorenz system shows the property of sensitivity to initial conditions and evolves in different mode with parameter variation, an equivalent system is introduced. The adaptive observer is …
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Quantitative analysis of the receptor-induced apoptotic decision network
… use a complex web of protein signaling pathways to interpret extracellular cues and decide and execute cell fates such as survival, apoptosis, differentiation, and proliferation. Cell decisions can be triggered by subtle, transient signals that are context specific, making them hard to study by …
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Modeling dynamic community acceptance of mining using agent-based modeling
<p>"This research attempts to provide fundamental understanding into the relationship between perceived sustainability of mineral projects and community acceptance. The main objective is to apply agent-based modeling (ABM) and discrete choice modeling to understand changes in community acceptance …
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Reconstructing dynamics from data: Algorithms for the prediction, estimation, and correction of chaotic systems
… of chaotic systems are of considerable interest to many disciplines. The interacting effects of sensitivity to initial conditions, imperfect modelling, and noisy observations cause challenges for their application. As sensors become ubiquitous, approaching this problem from a data-driven …
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The role of vegetation dynamics in the climate of West Africa
… and biospheric climate. The model is then used to study the dynamics of the coupled biosphere-atmosphere system over West Africa. Based on the model sensitivity to initial conditions and the resilience of the coupled system with respect to perturbations, we demonstrate that the coupled …
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Describing and forecasting patient census: Comparing linear and nonlinear analysis results
… phenomena. Scientists applying chaos theory to management agree that nonlinear analysis provides new information, they do not agree, however, on what statistical analysis should be computed or what type of forecasting is possible. This study assessed the applicability of several nonlinear …
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Lattice models of glasses and Potts models for community detection
… we denote t154. By examining the relaxation, atomic motion, Stokes-Einstein relationship violation, time-dependent displacement (van Hove function), wavevector-dependent relaxation, and multi-point correlations S4 and chi4, we can show that this new model satisfies all minimal requirements set …
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A Study of Evolutionary Contingency: Characterisation, Mechanisms, and Evidence
… of contingency by revealing their failure to entail the `biological evitability' of outcomes. I diagnose the issue under a dynamical systems framework to make explicit an often-overlooked fact. This is that any evolutionary outcome is preceded by a series of antecedents, which may have …
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Learning and memory in chaotic spiking neural models
The broad aim of this research is to explore the possibilities of using the properties that chaos provides to carry out information processing tasks. This has been done in the context of chaotic spiking neural networks (CSNNs). Chaos provides many interesting properties that can be used to achieve …
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Historical Consistent Neural Networks for Wind Power Prediction
… systems remains a fundamental challenge due to their inherent non-linearity, sensitivity to initial conditions, and long-range temporal dependencies. While Recurrent Neural Networks (RNNs) are widely used for such tasks, they often suffer from temporal inconsistency: relying on external …
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Analysis and synthesis techniques of nonlinear dynamical systems with applications to diagnostic of controlled thermonuclear fusion reactors
Nonlinear dynamical systems are of wide interest to engineers, physicists and mathematicians, and this is due to the fact that most of physical systems in nature are inherently non-linear. The nonlinearity of these systems has consequences on their time-evolution, which in some cases can be …