by Chung, Fu Lai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 166-174). / ACKNOWLEDGEMENT --- p.iii / ABSTRACT --- p.iv / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Integration of Fuzzy Systems and Neural Networks --- p.1 / Chapter 1.2 --- Objectives of the Research --- p.7 / Chapter 1.2.1 --- Fuzzification of Competitive Learning Algorithms --- p.7 / Chapter 1.2.2 --- Capacity Analysis of FAM and FRNS Models --- p.8 / Chapter 1.2.3 --- Structure and Parameter Identifications of FRNS --- p.9 / Chapter 1.3 --- Outline of the Thesis --- p.9 / Chapter 2. --- A Fuzzy System Primer --- p.11 / Chapter 2.1 --- Basic Concepts of Fuzzy Sets --- p.11 / Chapter 2.2 --- Fuzzy Set-Theoretic Operators --- p.15 / Chapter 2.3 --- "Linguistic Variable, Fuzzy Rule and Fuzzy Inference" --- p.19 / Chapter 2.4 --- Basic Structure of a Fuzzy System --- p.22 / Chapter 2.4.1 --- Fuzzifier --- p.22 / Chapter 2.4.2 --- Fuzzy Knowledge Base --- p.23 / Chapter 2.4.3 --- Fuzzy Inference Engine --- p.24 / Chapter 2.4.4 --- Defuzzifier --- p.28 / Chapter 2.5 --- Concluding Remarks --- p.29 / Chapter 3. --- Categories of Fuzzy Neural Systems --- p.30 / Chapter 3.1 --- Introduction --- p.30 / Chapter 3.2 --- Fuzzification of Neural Networks --- p.31 / Chapter 3.2.1 --- Fuzzy Membership Driven Models --- p.32 / Chapter 3.2.2 --- Fuzzy Operator Driven Models --- p.34 / Chapter 3.2.3 --- Fuzzy Arithmetic Driven Models --- p.35 / Chapter 3.3 --- Layered Network Implementation of Fuzzy Systems --- p.36 / Chapter 3.3.1 --- Mamdani's Fuzzy Systems --- p.36 / Chapter 3.3.2 --- Takagi and Sugeno's Fuzzy Systems --- p.37 / Chapter 3.3.3 --- Fuzzy Relation Based Fuzzy Systems --- p.38 / Chapter 3.4 --- Concluding Remarks --- p.40 / Chapter 4. --- Fuzzification of Competitive Learning Networks --- p.42 / Chapter 4.1 --- Introduction --- p.42 / Chapter 4.2 --- Crisp Competitive Learning --- p.44 / Chapter 4.2.1 --- Unsupervised Competitive Learning Algorithm --- p.46 / Chapter 4.2.2 --- Learning Vector Quantization Algorithm --- p.48 / Chapter 4.2.3 --- Frequency Sensitive Competitive Learning Algorithm --- p.50 / Chapter 4.3 --- Fuzzy Competitive Learning --- p.50 / Chapter 4.3.1 --- Unsupervised Fuzzy Competitive Learning Algorithm --- p.53 / Chapter 4.3.2 --- Fuzzy Learning Vector Quantization Algorithm --- p.54 / Chapter 4.3.3 --- Fuzzy Frequency Sensitive Competitive Learning Algorithm --- p.58 / Chapter 4.4 --- Stability of Fuzzy Competitive Learning --- p.58 / Chapter 4.5 --- Controlling the Fuzziness of Fuzzy Competitive Learning --- p.60 / Chapter 4.6 --- Interpretations of Fuzzy Competitive Learning Networks --- p.61 / Chapter 4.7 --- Simulation Results --- p.64 / Chapter 4.7.1 --- Performance of Fuzzy Competitive Learning Algorithms --- p.64 / Chapter 4.7.2 --- Performance of Monotonically Decreasing Fuzziness Control Scheme --- p.74 / Chapter 4.7.3 --- Interpretation of Trained Networks --- p.76 / Chapter 4.8 --- Concluding Remarks --- p.80 / Chapter 5. --- Capacity Analysis of Fuzzy Associative Memories --- p.82 / Chapter 5.1 --- Introduction --- p.82 / Chapter 5.2 --- Fuzzy Associative Memories (FAMs) --- p.83 / Chapter 5.3 --- Storing Multiple Rules in FAMs --- p.87 / Chapter 5.4 --- A High Capacity Encoding Scheme for FAMs --- p.90 / Chapter 5.5 --- Memory Capacity --- p.91 / Chapter 5.6 --- Rule Modification --- p.93 / Chapter 5.7 --- Inference Performance --- p.99 / Chapter 5.8 --- Concluding Remarks --- p.104 / Chapter 6. --- Capacity Analysis of Fuzzy Relational Neural Systems --- p.105 / Chapter 6.1 --- Introduction --- p.105 / Chapter 6.2 --- Fuzzy Relational Equations and Fuzzy Relational Neural Systems --- p.107 / Chapter 6.3 --- Solving a System of Fuzzy Relational Equations --- p.109 / Chapter 6.4 --- New Solvable Conditions --- p.112 / Chapter 6.4.1 --- Max-t Fuzzy Relational Equations --- p.112 / Chapter 6.4.2 --- Min-s Fuzzy Relational Equations --- p.117 / Chapter 6.5 --- Approximate Resolution --- p.119 / Chapter 6.6 --- System Capacity --- p.123 / Chapter 6.7 --- Inference Performance --- p.125 / Chapter 6.8 --- Concluding Remarks --- p.127 / Chapter 7. --- Structure and Parameter Identifications of Fuzzy Relational Neural Systems --- p.129 / Chapter 7.1 --- Introduction --- p.129 / Chapter 7.2 --- Modelling Nonlinear Dynamic Systems by Fuzzy Relational Equations --- p.131 / Chapter 7.3 --- A General FRNS Identification Algorithm --- p.138 / Chapter 7.4 --- An Evolutionary Computation Approach to Structure and Parameter Identifications --- p.139 / Chapter 7.4.1 --- Guided Evolutionary Simulated Annealing --- p.140 / Chapter 7.4.2 --- An Evolutionary Identification (EVIDENT) Algorithm --- p.143 / Chapter 7.5 --- Simulation Results --- p.146 / Chapter 7.6 --- Concluding Remarks --- p.158 / Chapter 8. --- Conclusions --- p.159 / Chapter 8.1 --- Summary of Contributions --- p.160 / Chapter 8.1.1 --- Fuzzy Competitive Learning --- p.160 / Chapter 8.1.2 --- Capacity Analysis of FAM and FRNS --- p.160 / Chapter 8.1.3 --- Numerical Identification of FRNS --- p.161 / Chapter 8.2 --- Further Investigations --- p.162 / Appendix A Publication List of the Candidate --- p.164 / BIBLIOGRAPHY --- p.166

by Kwok-leung Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 60-63). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Model --- p.7 / Chapter 2.1 --- Basic Beliefs on the vaguely classified variable --- p.7 / Chapter 2.2 --- Properties of the contingency table --- p.13 / Chapter 2.3 --- Mathematical model --- p.21 / Chapter Chapter 3 --- Simulation --- p.32 / Chapter 3.1 --- Likelihood function for the model and the simulation method --- p.32 / Chapter 3.2 --- Simulation result --- p.48 / Chapter Chapter 4 --- Discussion --- p.57 / Reference --- p.60

Chow Ying-ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 78-83). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objectives - Visual Navigation for Legged Robots --- p.1 / Chapter 1.2 --- Summary of Results --- p.3 / Chapter 1.3 --- Hardware Issues --- p.4 / Chapter 1.4 --- Contributions --- p.4 / Chapter 1.5 --- Organization of the Thesis --- p.4 / Chapter Chapter 2 --- Previous Work --- p.6 / Chapter 2.1 --- Vision Based Navigation --- p.6 / Chapter 2.1.1 --- Homography --- p.7 / Chapter 2.1.2 --- Ground Plane Obstacle Detection --- p.9 / Chapter 2.1.3 --- Regression --- p.12 / Chapter 2.2 --- Control Strategy --- p.13 / Chapter Chapter 3 --- System Overview --- p.16 / Chapter Chapter 4 --- Obstacle Detection by Fast Homography Estimation --- p.20 / Chapter 4.1 --- Ground Feature Extraction --- p.21 / Chapter 4.2 --- Ground Feature Correspondence --- p.21 / Chapter 4.3 --- Ground Homography Estimation --- p.24 / Chapter 4.3.1 --- Input point transformation --- p.24 / Chapter 4.3.2 --- Initial estimation --- p.26 / Chapter 4.3.3 --- Robust estimation --- p.27 / Chapter 4.4 --- Obstacle Detection --- p.29 / Chapter 4.5 --- Local Obstacle Map (LOM) on Ground --- p.33 / Chapter 4.5.1 --- Extraction from accumulative evidence --- p.34 / Chapter 4.5.2 --- Time-delay compensation --- p.34 / Chapter Chapter 5 --- Obstacle Avoidance by a Fuzzy Controller --- p.36 / Chapter 5.1 --- Gait Pattern --- p.38 / Chapter 5.2 --- Fuzzy Logic Controller --- p.42 / Chapter 5.2.1 --- Controller Inputs --- p.42 / Chapter 5.2.2 --- Controller Outputs --- p.43 / Chapter 5.2.3 --- Inference mechanism --- p.46 / Chapter Chapter 6 --- Implementation --- p.49 / Chapter 6.1 --- Hardware components --- p.49 / Chapter 6.1.1 --- VisionBug --- p.49 / Chapter 6.1.2 --- RF transmitter / receiver modules: --- p.52 / Chapter 6.2 --- Perception --- p.55 / Chapter 6.3 --- Image Calibration --- p.56 / Chapter 6.4 --- Motion Calibration: --- p.58 / Chapter 6.5 --- Software Programs --- p.66 / Chapter 6.5.1 --- Computational complexity --- p.68 / Chapter Chapter 7 --- Experimental Results --- p.69 / Chapter 7.1 --- Real Navigation Experiments --- p.70 / Chapter 7.2 --- Error Analysis of LOM --- p.73 / Chapter Chapter 8 --- Conclusion and future work --- p.76

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

Since the inception of the Internet there has been immense growth in the number of internet users worldwide, and the integration of social media in our daily lives has become commonplace for many. Yet, alongside the many benefits of this global connectivity come numerous risks. Research shows that individuals of all ages are exposed to, and engage in, risky activities online, despite numerous campaigns to highlight the perils of risky online behaviour. Although the rates of victimisation increase year-on-year, surprisingly little is known about the psychological mechanisms underlying online risk-taking. The work in this thesis aimed to address this gap in the psychological literature by conducting empirical research focussing on online risky behaviour and decision making across the lifespan. Four studies, conducted with individuals ranging in age from 13- to 79-years-old, investigated two online risk-taking behaviours, personal information disclosure and friending strangers, within the framework of Fuzzy Trace Theory. A further study investigated the posting of risky and inappropriate content online in British and Italian students, examining the role of self-monitoring and impulsivity. The work in this thesis reveals that Fuzzy Trace Theory is able to predict risk-taking and risk-averse behavioural intentions, and that the retrieval of gist-based, intuitive beliefs and values about online risk reduces risk-taking behaviour and intentions, whereas representing risk in a quantitative-based, verbatim manner leads to increased risk-taking intentions. The ability to reason using gist representations increases with age. Additionally, high self-monitoring was found to predict risky posting behaviour across different cultures. These findings offer a novel and important contribution to our theoretical and practical knowledge about risky online behaviour, and have the potential to inform the development of more effective online safety intervention programmes.

Au Yeung Ching Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 97-110). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Semantic Web and Ontologies --- p.3 / Chapter 1.2 --- Motivations --- p.5 / Chapter 1.2.1 --- Fuzziness of Concepts --- p.6 / Chapter 1.2.2 --- Typicality of Objects --- p.6 / Chapter 1.2.3 --- Context and Its Effect on Reasoning --- p.8 / Chapter 1.3 --- Objectives --- p.9 / Chapter 1.4 --- Contributions --- p.10 / Chapter 1.5 --- Structure of the Thesis --- p.11 / Chapter 2 --- Background Study --- p.13 / Chapter 2.1 --- The Semantic Web --- p.14 / Chapter 2.2 --- Ontologies --- p.16 / Chapter 2.3 --- Description Logics --- p.20 / Chapter 2.4 --- Fuzzy Set Theory --- p.23 / Chapter 2.5 --- Concepts and Categorization in Cognitive Psychology --- p.25 / Chapter 2.5.1 --- Theory of Concepts --- p.26 / Chapter 2.5.2 --- Goodness of Example versus Degree of Typicality --- p.28 / Chapter 2.5.3 --- Similarity between Concepts --- p.29 / Chapter 2.5.4 --- Context and Context Effects --- p.31 / Chapter 2.6 --- Handling of Uncertainty in Ontologies and Description Logics --- p.33 / Chapter 2.7 --- Typicality in Models for Knowledge Representation --- p.35 / Chapter 2.8 --- Semantic Similarity in Ontologies and the Semantic Web --- p.39 / Chapter 2.9 --- Contextual Reasoning --- p.41 / Chapter 3 --- A Formal Model of Ontology --- p.44 / Chapter 3.1 --- Rationale --- p.45 / Chapter 3.2 --- Concepts --- p.47 / Chapter 3.3 --- Characteristic Vector and Property Vector --- p.47 / Chapter 3.4 --- Subsumption of Concepts --- p.49 / Chapter 3.5 --- Likeliness of an Individual in a Concept --- p.51 / Chapter 3.6 --- Prototype Vector and Typicality --- p.54 / Chapter 3.7 --- An Example --- p.59 / Chapter 3.8 --- Similarity between Concepts --- p.61 / Chapter 3.9 --- Context and Contextualization of Ontology --- p.65 / Chapter 3.9.1 --- Formal Definitions --- p.67 / Chapter 3.9.2 --- Contextualization of an Ontology --- p.69 / Chapter 3.9.3 --- "Contextualized Subsumption Relations, Likeliness, Typicality and Similarity" --- p.71 / Chapter 4 --- Discussions and Analysis --- p.73 / Chapter 4.1 --- Properties of the Formal Model for Fuzzy Ontologies --- p.73 / Chapter 4.2 --- Likeliness and Typicality --- p.78 / Chapter 4.3 --- Comparison between the Proposed Model and Related Works --- p.81 / Chapter 4.3.1 --- Comparison with Traditional Ontology Models --- p.81 / Chapter 4.3.2 --- Comparison with Fuzzy Ontologies and DLs --- p.82 / Chapter 4.3.3 --- Comparison with Ontologies modeling Typicality of Objects --- p.83 / Chapter 4.3.4 --- Comparison with Ontologies modeling Context --- p.84 / Chapter 4.3.5 --- Limitations of the Proposed Model --- p.85 / Chapter 4.4 --- "Significance of Modeling Likeliness, Typicality and Context in Ontologies" --- p.86 / Chapter 4.5 --- Potential Application of the Model --- p.88 / Chapter 4.5.1 --- Searching in the Semantic Web --- p.88 / Chapter 4.5.2 --- Benefits of the Formal Model of Ontology --- p.90 / Chapter 5 --- Conclusions and Future Work --- p.91 / Chapter 5.1 --- Conclusions --- p.91 / Chapter 5.2 --- Future Research Directions --- p.93 / Publications --- p.96 / Bibliography --- p.97

"Consider two people having a face-to-face conversation. They sometimes listen, sometimes talk, and sometimes interrupt each other. They use facial expressions to signal that they are confused. They point at objects. They jump from topic to topic opportunistically. When another acquaintance walks by, they nod and say hello. All the while they have other concerns on their mind, such as not missing the meeting that starts in 10 minutes. Like many other humans behaviors, these are not easy to replicate in artificial agents. In this work we look into the design requirements of an embodied agent that can participate in such natural conversations in a mixed-initiative, multi-modal setting. Such an agent needs to understand participating in a conversation is not merely a matter of sending a message and then waiting to receive a response -- both partners are simultaneously active at all times. This agent should be able to deal with different, sometimes conflicting goals, and be always ready to address events that may interrupt the current topic of conversation. To address those requirements, we have created a modular architecture that includes distributed functional units that compete with each other to gain control over available resources. Each of these units, called a schema, has its own sense- think-act cycle. In the field of robotics, this design is often referred to as "behavior-based" or "schema-based." The major contribution of this work is merging behavior-based robotics with plan- based human-computer interaction."

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

O presente trabalho tem como objetivo explorar o uso de técnicas de controle avançados na indústria siderúrgica. Propõe-se uma estratégia de controle do nível do aço da piscina formada entre os rolos de um sistema lingotamento contínuo de tiras (LCT) utilizando a tecnologia twin-roll (rolos duplos). O processo LCT rolos duplos tem por finalidade a produção de tiras solidificadas de espessura constante sob uma força de separação entre os rolos também constante. O nível de aço bem como a força de separação são as variáveis mais críticas para a produção de tiras de aço de alta qualidade. O nível pode ser controlado usando a entrada de aço ou a velocidade de laminação. Entretanto, a velocidade de laminação é usualmente utilizada para regular a força de separação entre os rolos. A estratégia de controle proposta inclui a incorporação de um tundish intermediário submerso na piscina. O controle do nível é então feito a partir da saída de aço do tundish intermediário. Consideramos as técnicas de controle linearizante por realimentação de estado e de controle fuzzy usando ambos os modelos Takagi-Sugeno (T-S) e Mamdani. Resultados de simulação são apresentados para uma planta instalada no Instituto de Pesquisa Tecnológica (IPT) de São Paulo, divisão de metalurgia (DIMET). / The aim of this work is to explore the use of advanced control techniques in the metallurgical industry. A control strategy to regulate the molten steellevel of a strip-casting process is proposed. The process produces a solidified strip of constant thickness given by the roll gap under a constant roll separation force. Along with the molten steel level the rool separation force are the most criticaI process variables. The molten steel level may be controlled using the tundish output flow or the casting speed. However, the casting speed is usually used to control the roll force separation. In the control strategy proposed it is incorporated an intermediary tundish submerse into the pool between the rotating rolls to improve the strip thickness uniformity. The molten steel level is thus controlled by the intermediary tundish output flow. Conventional PI, feedback linearizing plus a fuzzy control term and a fuzzy controller in a cascade configuration are considered. Simulation results are presented considering the real system parameters of a plant installed at the Instituto de Pesquisa Tecnológica (IPT) de São Paulo, Divisão de Metalurgia (DIMET).

Yang, Fan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 100-106). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature review --- p.1 / Chapter 1.2 --- The main contribution of this thesis --- p.5 / Chapter 1.3 --- Relations between the above three models --- p.7 / Chapter 2 --- Model 1 --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.2 --- Minimax rule risk function --- p.11 / Chapter 2.3 --- Fuzzy liquidity of asset --- p.12 / Chapter 2.4 --- Notations --- p.15 / Chapter 2.5 --- Model formulation --- p.16 / Chapter 2.6 --- Numerical example and result --- p.25 / Chapter 3 --- Model 2 --- p.36 / Chapter 3.1 --- Introduction --- p.36 / Chapter 3.2 --- Notations --- p.39 / Chapter 3.3 --- Model formulation --- p.41 / Chapter 3.4 --- Numerical example and result --- p.45 / Chapter 4 --- Model 3 --- p.51 / Chapter 4.1 --- Introduction --- p.51 / Chapter 4.2 --- Notations --- p.52 / Chapter 4.3 --- Model formulation --- p.54 / Chapter 4.4 --- Numerical example and result --- p.62 / Chapter 5 --- Conclusion --- p.68 / Chapter A --- Source Data for Model 1 --- p.71 / Chapter B --- Source Data for Model 2 --- p.80 / Chapter C --- Source Data for Model 3 --- p.90 / Bibliography --- p.100