Thesis (M.S.)--Mechanical Engineering, Georgia Institute of Technology, 2005. / Esogbue, Augustine, Committee Member ; Lee, Kok-Meng, Committee Member ; Ye-Hwa Chen, Committee Chair. Includes bibliographical references.

Thesis (M.B.A.)--University of Hong Kong, 1993.

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Mavris, Dimitri; Committee Member: Bishop, Carlee; Committee Member: Brown, David; Committee Member: Costello, Mark; Committee Member: Schrage, Daniel. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--University of New South Wales, 1998. / Completed at: University of New South Wales, School of Information, Library, and Archive Studies, 1998. Title from electronic deposit form.

Thesis (Ph.D.)--University of Central Florida, 2008. / Adviser: Lesia L. Crumpton-Young. Includes bibliographical references (p. 219-230).

Thesis (M.Sc. (Mathematics)) - Rhodes University, 2009.

Thesis (Ph. D.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.

Magister Commercii - MCom / With the transition to a democratic society, one of the challenges to emerge was to provide economic freedoms for all – that is, freedom from isolation, freedom from powerlessness, freedom from hunger, and freedom from poverty. Fighting poverty has always been high on the agenda of the democratic government since the transition. Numerous measures and policies were instituted aimed at reducing poverty. Researchers initiated serious efforts to try and understand the nature and extent of poverty, through various studies being conducted. Many of these studies focused only on money-metric measures, despite the fact that poverty is a multidimensional concept. What has resulted are various poverty-measurement methods producing different results on the extent of poverty. In addition, certain aspects of these money-metric approaches have serious shortcomings. The fuzzy sets approach addresses many of these shortcomings, as it is a multidimensional approach. Few studies have used this approach to measure poverty in South Africa. This thesis plans to use this method to analyse poverty levels and trends in South Africa, focusing on multidimensional, non-money-metric poverty. Data from Census 1996, Census 2001 and Community Survey 2007 will be used for the study. From the results of the analysis it was established that there is some divergence in the findings of money-metric approaches and those of fuzzy sets. A key result to emerge is the difference in poverty trends over the period 1996 – 2007. Most studies reviewed in Chapter Three that used the money-metric approach showed that poverty trends were upward in the 1990s, before a downward trend took place in the 2000s. This took place irrespective of the survey data used. The non-money-metric poverty trends derived in this chapter, however, show a continuous downward trend over the period. The overall mean deprivation in South Africa has declined since 1996. For people residing in provinces such as Limpopo, the Eastern Cape and KwaZulu-Natal, they are more likely to be poor under the fuzzy sets approach. This may be an indication of inadequate service delivery and the extent to which recent government measures to address poverty have been successful or not. In terms of race, blacks still have the highest mean deprivation, but enjoyed the biggest decline of mean deprivation between 1996 and 2007. Finally, mean deprivation for female-headed households in South Africa was also higher than for male-headed households over the period.

published_or_final_version / Economics and Finance / Master / Master of Economics

by Lee Tan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves [128]-[130] / Chapter CHAPTER 1 --- INTRODUCTION --- p.1-1 / Chapter 1.1 --- Introduction to Artificial Neural Networks --- p.1-4 / Chapter 1.1.1 --- Fundamentals of Artificial Neural Networks --- p.1-5 / Chapter 1.1.2 --- Various Artificial Neural Network Models ´ؤA Review --- p.1-11 / Chapter 1.2 --- Introduction to Fuzzy Sets Theory --- p.1-17 / Chapter 1.2.1 --- "Fuzziness, Fuzzy sets and Membership Function" --- p.1-17 / Chapter 1.2.2 --- Applications of Fuzzy Sets --- p.1-19 / Connective Summary --- p.1-21 / Chapter CHAPTER 2 --- LEARNING WITH FUZZY INFORMATION --- p.2-1 / Chapter 2.1 --- "Decision Making, Pattern Associating and Pattern Classification" --- p.2-3 / Chapter 2.2 --- Artificial Neural Networks as Learning Decision Systems --- p.2-6 / Chapter 2.3 --- Fuzziness in Decision Making Processes --- p.2-10 / Chapter 2.4 --- Learning with Fuzzy Information --- p.2-12 / Chapter 2.5 --- The Formulation of Our Approach --- p.2-16 / Connective Summary --- p.2-18 / Chapter CHAPTER 3 --- A MODIFIED BACKPROPAGATION ALGORITHM FOR MULTILAYER FEEDFORWARD NETWORKS --- p.3-1 / Chapter 3.1 --- Preliminaries --- p.3-3 / Chapter 3.2 --- The Error Backpropagation Algorithm (EBPA) --- p.3-8 / Chapter 3.3 --- A Modified EBPA Learning with A Priori Fuzzy Information --- p.3-11 / Chapter 3.3.1 --- The Membership-Weighed Objective Function --- p.3-11 / Chapter 3.3.2 --- The Fuzzy Error Backpropagation Algorithm --- p.3-13 / Chapter 3.4 --- Discussion on the Proposed Fuzzy EBPA --- p.3-15 / Chapter 3.4.1 --- Methods of Determining Membership Functions --- p.3-15 / Chapter 3.4.2 --- Fuzzy EBPA Alters the Effective Target Patterns --- p.3-19 / Chapter 3.4.3 --- Estimating the Learning Rates Required for the Fuzzy EBPA --- p.3-21 / Connective Summary --- p.3-24 / Chapter CHAPTER 4 --- APPLICATION EXAMPLES --- p.4-1 / Chapter 4.1 --- A Single Node Classifier --- p.4-2 / Chapter 4.2 --- The Fuzzy XOR Problem --- p.4-29 / Chapter 4.2.1 --- Network Configuration 1 --- p.4-36 / Chapter 4.2.2 --- Network Configuration 2 --- p.4-46 / Chapter 4.2.3 --- Comments on the Simulation Results --- p.4-50 / Chapter 4.3 --- A Speech Recognition System --- p.4-54 / Connective Summary --- p.4-59 / Chapter CHAPTER 5 --- DISCUSSION AND CONCLUSION --- p.5-1