No description available.

No description available.

No description available.

No description available.

No description available.

Tse, Ming Fun. / Thesis submitted in: December 2001. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 142-146). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Definition --- p.2 / Chapter 1.2 --- Contributions --- p.3 / Chapter 1.3 --- Thesis Outline --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Representing Inexact Knowledge --- p.7 / Chapter 2.1.1 --- Nature of Inexact Knowledge --- p.7 / Chapter 2.1.2 --- Probability Based Reasoning --- p.8 / Chapter 2.1.3 --- Certainty Factor Algebra --- p.11 / Chapter 2.1.4 --- Fuzzy Logic --- p.13 / Chapter 2.2 --- Machine Learning Paradigms --- p.13 / Chapter 2.2.1 --- Classifications --- p.14 / Chapter 2.2.2 --- Neural Networks and Gradient Descent --- p.15 / Chapter 2.3 --- Related Learning Systems --- p.21 / Chapter 2.3.1 --- Relational Concept Learning --- p.21 / Chapter 2.3.2 --- Learning of Fuzzy Concepts --- p.24 / Chapter 2.4 --- Fuzzy Logic --- p.26 / Chapter 2.4.1 --- Fuzzy Set --- p.27 / Chapter 2.4.2 --- Basic Notations in Fuzzy Logic --- p.29 / Chapter 2.4.3 --- Basic Operations on Fuzzy Sets --- p.29 / Chapter 2.4.4 --- "Fuzzy Relations, Projection and Cylindrical Extension" --- p.31 / Chapter 2.4.5 --- Fuzzy First Order Logic and Fuzzy Prolog --- p.34 / Chapter 3 --- Knowledge Representation and Learning Algorithm --- p.43 / Chapter 3.1 --- Knowledge Representation --- p.44 / Chapter 3.1.1 --- Fuzzy First-order Logic ´ؤ A Powerful Language --- p.44 / Chapter 3.1.2 --- Literal Forms --- p.48 / Chapter 3.1.3 --- Continuous Variables --- p.50 / Chapter 3.2 --- System Architecture --- p.61 / Chapter 3.2.1 --- Data Reading --- p.61 / Chapter 3.2.2 --- Preprocessing and Postprocessing --- p.67 / Chapter 4 --- Global Evaluation of Literals --- p.71 / Chapter 4.1 --- Existing Closeness Measures between Fuzzy Sets --- p.72 / Chapter 4.2 --- The Error Function and the Normalized Error Functions --- p.75 / Chapter 4.2.1 --- The Error Function --- p.75 / Chapter 4.2.2 --- The Normalized Error Functions --- p.76 / Chapter 4.3 --- The Nodal Characteristics and the Error Peaks --- p.79 / Chapter 4.3.1 --- The Nodal Characteristics --- p.79 / Chapter 4.3.2 --- The Zero Error Line and the Error Peaks --- p.80 / Chapter 4.4 --- Quantifying the Nodal Characteristics --- p.85 / Chapter 4.4.1 --- Information Theory --- p.86 / Chapter 4.4.2 --- Applying the Information Theory --- p.88 / Chapter 4.4.3 --- Upper and Lower Bounds of CE --- p.89 / Chapter 4.4.4 --- The Whole Heuristics of FF99 --- p.93 / Chapter 4.5 --- An Example --- p.94 / Chapter 5 --- Partial Evaluation of Literals --- p.99 / Chapter 5.1 --- Importance of Covering in Inductive Learning --- p.100 / Chapter 5.1.1 --- The Divide-and-conquer Method --- p.100 / Chapter 5.1.2 --- The Covering Method --- p.101 / Chapter 5.1.3 --- Effective Pruning in Both Methods --- p.102 / Chapter 5.2 --- Fuzzification of FOIL --- p.104 / Chapter 5.2.1 --- Analysis of FOIL --- p.104 / Chapter 5.2.2 --- Requirements on System Fuzzification --- p.107 / Chapter 5.2.3 --- Possible Ways in Fuzzifing FOIL --- p.109 / Chapter 5.3 --- The α Covering Method --- p.111 / Chapter 5.3.1 --- Construction of Partitions by α-cut --- p.112 / Chapter 5.3.2 --- Adaptive-α Covering --- p.112 / Chapter 5.4 --- The Probabistic Covering Method --- p.114 / Chapter 6 --- Results and Discussions --- p.119 / Chapter 6.1 --- Experimental Results --- p.120 / Chapter 6.1.1 --- Iris Plant Database --- p.120 / Chapter 6.1.2 --- Kinship Relational Domain --- p.122 / Chapter 6.1.3 --- The Fuzzy Relation Domain --- p.129 / Chapter 6.1.4 --- Age Group Domain --- p.134 / Chapter 6.1.5 --- The NBA Domain --- p.135 / Chapter 6.2 --- Future Development Directions --- p.137 / Chapter 6.2.1 --- Speed Improvement --- p.137 / Chapter 6.2.2 --- Accuracy Improvement --- p.138 / Chapter 6.2.3 --- Others --- p.138 / Chapter 7 --- Conclusion --- p.140 / Bibliography --- p.142 / Chapter A --- C4.5 to FOIL File Format Conversion --- p.147 / Chapter B --- FF99 example --- p.150

Orientador: Luís Roberto Almeida Gabriel Filho / Resumo: Em virtude das mudanças climáticas que vem ocorrendo e da necessidade de tecnologias que melhorem o manejo pecuário, o objetivo desta dissertação é elaborar um modelo matemático baseado em lógica Fuzzy capaz de fornecer instantaneamente o escore de fertilidade de touros a partir da avaliação do sêmen animal e, aplicá-lo a um conjunto de dados para validá-lo e verificar a influência das estações do ano na fertilidade. Para isso, o modelo considerou os limites estabelecidos pelo CBRA/MAPA para as variáveis Turbilhão, Motilidade, Vigor, Defeitos Maiores, Defeitos Menores e Defeitos Totais do sêmen, compondo uma base de regras de 735 combinações do tipo "Se...Então", pelo método de inferência Mamdani para determinar a Fertilidade Fuzzy, sendo aplicado a 152 amostras validas coletadas de touros da raça Nelore e Simental durante o período de um ano. Os resultados foram analisados por meio de testes estatísticos (teste Tukey, análises de variância, correlação, clusters, regressão e curva ROC), mostrando que o modelo é eficiente em seu objetivo de classificação e que as condições climáticas afetam os animais de maneira geral, sem indicadores significativos para diferenças entre as raças, sendo que as variáveis apresentaram-se bastante representativas ao modelo, possibilitando uma nova ferramenta de auxilio ao produtor. / Abstract: The objective of this dissertation is to elaborate a mathematical model based on Fuzzy logic capable of providing instantaneously the fertility score of bulls from the evaluation of the animal semen and apply it to a set of data to validate it and verify the influence of the seasons of the year on fertility. For this, the model considered the limits established by the CBRA / MAPA for the variables Turbidity, Motility, Vigor, Major Defects, Minor Defects and Total Defects of the semen, composing a rule base of 735 "If ... Then", combined by the Mamdani inference method to determine Fuzzy Fertility, being applied to 152 valid samples collected from Nelore and Simmental bulls during a one year period. The results were analyzed by means of statistical tests (Tukey test, analysis of variance, correlation, clusters, regression and ROC curve) showing that the model is efficient in its classification objective and that the climatic conditions affect the animals in general, without significant indicators for differences among races, and the variables were quite representative of the model, allowing a new tool to assist the producer. / Mestre

Thesis (M.S.)--Baylor University, 2005.

Thesis (Ph. D.)--University of Rhode Island, 2005. / Typescript. Includes bibliographical references (v. 2: leaves 582-593).

The Crew Exploration Vehicle (CEV) necessitates higher levels of automation than previous NASA vehicles due to program requirements for automation, including Automated Rendezvous and Docking (AR&D). Studies of spacecraft development often point to the locus of decision-making authority between humans and computers (i.e. automation) as a prime driver for cost, safety, and mission success. Therefore, a critical component in the CEV development is the determination of the correct level of automation. To identify the appropriate levels of automation and autonomy to design into a human space flight vehicle, NASA has created the Function-specific Level of Autonomy and Automation Tool (FLOAAT). This research develops a methodology for prototyping increased levels of automation for spacecraft rendezvous functions. This methodology was used to evaluate the accuracy of the FLOAAT-specified levels of automation, via prototyping. Two spacecraft rendezvous planning tasks were selected and then prototyped in Matlab using Fuzzy Logic (FL) techniques and existing Shuttle rendezvous trajectory algorithms. The prototyped functions are the determination of the maximum allowable Timeof- IGnition (TIG) slip for a rendezvous phasing burn and the evaluation of vehicle position relative to Transition initiation (Ti) position constraints. The methodology for prototyping rendezvous functions at higher levels of automation is judged to be a promising technique. The results of the prototype indicate that the FLOAAT recommended level of automation is reasonably accurate and that FL can be effectively used to model human decision-making used in spacecraft rendezvous. FL has many desirable attributes for modeling human decision-making, which makes it an excellent candidate for additional spaceflight automation applications. These conclusions are described in detail as well as recommendations for future improvements to the FLOAAT method and prototyped rendezvous functions.