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Until recently, methodologies to extract sub-pixel information from remotely sensed data have focused on linear un-mixing models and so called fuzzy classifiers. Recent research has suggested that neural networks have the potential for providing sub- pixel information. Neural networks offer an attractive alternative as they are non- parametric, they are not restricted to any number of classes, they do not assume that the spectral signatures of pixel components mix linearly and they do not necessarily have to be trained with pure pixels. The thesis tests the validity of neural networks for extracting sub-pixel information using a combination of qualitative and quantitative analysis tools. Previously published experiments use data sets that are often limited in terms of numbers of pixels and numbers of classes. The data sets used in the thesis reflect the complexity of the landscape. Preparation for the experiments is canied out by analysing the data sets and establishing that the network is not sensitive to particular choices of parameters. Classification results using a conventional type of target with which to train the network show that the response of the network to mixed pixels is different from the response of the network to pure pixels. Different target types are then tested. Although targets which provide detailed compositional information produce higher accuracies of classification for subsidiary classes, there is a trade off between the added information and added complexity which can decrease classification accuracy. Overall, the results show that the network seems to be able to identify the classes that are present within pixels but not their proportions. Experiments with a very accurate data set show that the network behaves like a pattern matching algorithm and requires examples of mixed pixels in the training data set in order to estimate pixel compositions for unseen pixels. The network does not function like an unmixing model and cannot interpolate between pure classes.

This paper attempts to show how fuzzy set theory can be used to weaken some of the stringent, rationality assumptions used in classical micro-economics. The objective of the paper is to see whether by introducing fuzziness we arrive to new results or just only generalizations of classical micro-economic results. We discover that the axiom of completeness is not needed anymore. Using fuzziness will also allow us to better explain the existing gap between delimiting possible choices and making the actual choice. We also introduce the notions of a fuzzy indifference set with a measurable area. The fuzzy utility surface is also discussed. The demand curve is now 'thick'. / In the producer area, the classical hypothesis that maximum profit entails maximum utility of profit is now substantially weakened when introducing fuzziness. / Finally, we consider revealed preference within a fuzzy context.

This thesis proposes a method for the design of a real-time neuro-fuzzy trajectory generator for the robotic rehabilitation of patients with upper limb dysfunction due to neurological diseases. The system utilizes a fuzzy-logic schema to introduce compliance into the human-robot interaction, and to allow the emulation of a wide variety of therapy techniques. This approach also allows for the fine-tuning of system dynamics using linguistic variables. The rule base for the system is trained using a fuzzy clustering algorithm and applied to experimental data gathered during traditional therapy sessions. The compliance rule base is combined with a hybrid neuro-fuzzy compensator to automatically tune the dynamics of the system. The trajectory generator is packaged as a platform-independent solution to facilitate the rehabilitation of patients using multiple manipulator configurations.

This thesis proposes a method for the design of a real-time neuro-fuzzy trajectory generator for the robotic rehabilitation of patients with upper limb dysfunction due to neurological diseases. The system utilizes a fuzzy-logic schema to introduce compliance into the human-robot interaction, and to allow the emulation of a wide variety of therapy techniques. This approach also allows for the fine-tuning of system dynamics using linguistic variables. The rule base for the system is trained using a fuzzy clustering algorithm and applied to experimental data gathered during traditional therapy sessions. The compliance rule base is combined with a hybrid neuro-fuzzy compensator to automatically tune the dynamics of the system. The trajectory generator is packaged as a platform-independent solution to facilitate the rehabilitation of patients using multiple manipulator configurations.

Thesis (Ph.D. - Hydrology and Water Resources)--University of Arizona. / Includes bibliographical references (leaves 185-192).

Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 22, 2008) Includes bibliographical references (p. 33-35).

Thesis (Ph.D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 23, 2009) Vita. Includes bibliographical references.