Thesis (Ph. D.)--University of Nevada, Reno, 2008. / "May 2008." Includes bibliographical references (leaves 86-91). Online version available on the World Wide Web.

Thesis (Ph.D.(Civil Engineering)) -- University of Pretoria, 2006.

Thesis (Ph. D.)--Carleton University, 2005. / Includes bibliographical references (p. 159-166). Also available in electronic format on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaf 54). Also available on the Internet.

Thesis (M. Phil.)--University of Hong Kong, 1996. / Includes bibliographical references (leaf 85-94).

Thesis (M.S.) in Computer Engineering--University of Maine, 2001. / Includes vita. Includes bibliographical references (leaves 67-69).

Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on May 7, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering and Intelligent Systems, Department of Electrical and Computer Engineering, University of Alberta. Includes bibliographical references.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaf 54). Also available on the Internet.

Fast and accurate recognition of the Statistical Control Chart Patterns (SPCCP) is significant for supervising manufacturing processes to accomplish better control and to make high value products. SPCCP can display eight kinds of patterns: normal, stratification, systematic, increasing trend, decreasing trend, up shift, down shift and cyclic. With the exception of the natural pattern, all other patterns indicate that the supervised manufacturing process is not performing properly and actions need to be taken to correct the problems. This research proposes new approaches, neural networks and neural-fuzzy systems, to the (SPCCP) recognition. This dissertation also investigates the use of features extracted from statistical analysis for simple patterns, and wavelet analysis for concurrent patterns as the components of the input vectors. Results based on simulated data show that the proposed approaches perform better than conventional approaches. Our work concluded that the extracted features improve the performance of the proposed recognizer systems.

The purpose of this paper is present the results of a developed, and implemented a Fuzzy rule based model to determine the probability of precipitation in the Marion Illinois area during the summer months. The model employs fuzzy logic and the results are compared to actual data to measure how reliable and viable this method is as an option in the precipitation prediction. Researchers, over the years, have been developing models for simulating, predicting and analyzing atmospheric phenomena, in order to accurately determine their immediate and long term effects on the environment and the quality of human life, such as in agriculture, ecosystem evolution, biodiversity, and disaster preparedness (decision support systems: drought/ warning or flash floods). To simulate global climate changes, researchers use General Circulation Models (GCMs). These have been developed using numerical weather predictions. These models are very useful for global impact studies such as global warming, but they are limited if applied to regional scenarios. This is because they are not able to neither simulate the local effects nor accurately present spatial and temporal resolutions. Continued work to improve the efficiency of these systems has led to the development of various models. Improvements have come in the form of Regional Climate Models; they have higher resolution and take into account orographic effects. These models use downscaling techniques, which bridge the gap between the global climate simulations and regional climate impact assessment. This paper presents the implementation of a fuzzy rule-based downscaling technique with specific application to the Marion, Illinois area. It shows that this method has the distinct advantage of being both computation and resource inexpensive while producing accurate information in a timely manner.