A pesquisa de cunho qualitativo aqui apresentada tem como propósito discutir o valor e o papel da lógica fuzzy na solução de problemas reais, dada a característica de suas ferramentas para lidar com questões subjetivas, uma vez que as soluções de problemas provenientes do mundo real estão carregadas de relações construídas no mundo interno do resolvedor são oriundas da subjetividade do sujeito resolvedor. Nosso trabalho teve como um dos objetivos responder às questões: Quais os pressupostos teóricos da teoria fuzzy e quais as possibilidades de reconhecimento de seu valor e de seu papel para a Educação Matemática? Como o pensamento matemático do aluno lida com o raciocínio fuzzy? Com essa perspectiva, aproximamo-nos dos alunos do Curso de Licenciatura das Faculdades Unificadas da Fundação Educacional de Barretos UNFEB , na busca de evidências, em termos de pesquisa, por meio de dois questionários e um minicurso ministrado pelo pesquisador, nesta ordem: questionário, minicurso e questionário. O primeiro questionário instigava o uso de variáveis subjetivas na solução de questões. O minicurso teve como foco central a resolução de um mesmo problema, utilizando matemática clássica e matemática fuzzy. O questionário final, de cunho avaliativo, verificava o uso, pelo aluno, das ferramentas da teoria fuzzy em problemas semelhantes aos anteriores. Os resultados da pesquisa indicaram a pouca experiência dos alunos com o raciocínio fuzzy dada, talvez, a dominância da matemática formal/determinística; mostraram, também, evidências de que os mesmos problemas, resolvidos verbalmente, sem o uso da matemática, revelam-se especialmente desafiadores quando é solicitada uma solução matemática fuzzy. / The qualitative research here presented intends to discuss the value and the role of fuzzy logic in the solution of real problems, due to the characteristic of its tools to deal with subjective questions, since the solution of problems originated in the real world are loaded with relationships built in the resolvers internal world they are originated in the subjectresolvers subjectivity. Our work had as one of its goals to answer the questions: Which are Fuzzy Theory theoretical assumptions and what are the possibilities of acknowledgement of its value and role to Mathematical Education?; How does the students mathematical thinking deal with fuzzy reasoning? On this perspective, we approached the students of the Curso de Licenciatura das Faculdades Unificadas da Fundação Educacional de Barretos - UNFEB searching for research evidences by means of two questionnaires and a mini-course taught by the researcher, in the following order: questionnaire, mini-course and questionnaire. The first questionnaire incited the use of subjective variables in the solution of questions. The mini-course, after the questionnaire, had as main focus the solution of one (same) problem using classical mathematics and fuzzy mathematics. The final questionnaire, of evaluative nature, verified the students use of fuzzy theory tools for problems similar to the previous ones. The research results indicated the students little experience with fuzzy reasoning maybe due to the dominance of formal/deterministic mathematics as well as showing evidences that the same problems solved verbally without the use of mathematics were especially challenging when a fuzzy mathematics solution was requested.

by Lee Wai Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 118-[124]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Review of Previous Work --- p.3 / Chapter 1.3 --- Scope of the Thesis --- p.4 / Chapter 2 --- Background Knowledge of Fuzzy Control System --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Fuzzy Sets --- p.7 / Chapter 2.2.1 --- Properties of Fuzzy Sets --- p.10 / Chapter 2.2.2 --- Operations on Fuzzy Sets --- p.13 / Chapter 2.3 --- Fuzzy Models --- p.14 / Chapter 2.3.1 --- Linguistic Model --- p.15 / Chapter 2.3.2 --- Takagi-Sugeno-Kang (TSK) Fuzzy Model --- p.16 / Chapter 2.4 --- Fuzzy Inference System --- p.17 / Chapter 2.4.1 --- Fuzzifier --- p.18 / Chapter 2.4.2 --- Knowledge Base --- p.19 / Chapter 2.4.3 --- Inference Engine --- p.19 / Chapter 2.4.4 --- Defuzzifier --- p.20 / Chapter 2.4.5 --- Product-Sum-Gravity Inference --- p.21 / Chapter 3 --- Decomposition of Fuzzy Rules --- p.25 / Chapter 3.1 --- Introduction --- p.25 / Chapter 3.2 --- Decomposability of Fuzzy Inference System --- p.26 / Chapter 3.3 --- The Decomposability condition --- p.29 / Chapter 3.4 --- Determining Decomposed Parameters --- p.32 / Chapter 3.5 --- Decomposable Approximation --- p.35 / Chapter 3.5.1 --- Linear Approximation --- p.38 / Chapter 3.5.2 --- Case Study --- p.40 / Chapter 3.6 --- Limitation of Decomposable Approximation --- p.42 / Chapter 3.7 --- Approximation Index --- p.44 / Chapter 3.7.1 --- Case Study --- p.48 / Chapter 3.8 --- Decomposable TSK Model --- p.52 / Chapter 3.8.1 --- Case Study --- p.54 / Chapter 3.9 --- Conclusion --- p.56 / Chapter 4 --- Fuzzy Identification --- p.58 / Chapter 4.1 --- Introduction --- p.58 / Chapter 4.2 --- Least-squares Estimation --- p.59 / Chapter 4.3 --- LSE Formulation of Various Fuzzy Models --- p.63 / Chapter 4.3.1 --- Linguistic Model --- p.63 / Chapter 4.3.2 --- TSK Model --- p.69 / Chapter 4.3.3 --- Decomposable System --- p.75 / Chapter 4.3.4 --- Comparative Case Study --- p.79 / Chapter 4.4 --- Fuzzy Regional System Identification --- p.81 / Chapter 4.4.1 --- Case Study --- p.86 / Chapter 4.5 --- Recursive Estimation --- p.86 / Chapter 4.5.1 --- Case Study --- p.90 / Chapter 4.6 --- Conclusion --- p.90 / Chapter 5 --- Performance-Based Fuzzy Gain Controller --- p.92 / Chapter 5.1 --- Introduction --- p.92 / Chapter 5.2 --- Conventional Fuzzy Control --- p.93 / Chapter 5.3 --- Fuzzy Gain Control --- p.95 / Chapter 5.4 --- Design Algorithm --- p.97 / Chapter 5.5 --- Stability Design Approach --- p.98 / Chapter 5.6 --- Simulation Case Study --- p.102 / Chapter 5.7 --- Conclusion --- p.106 / Chapter 6 --- Identification/Control Design Example --- p.107 / Chapter 7 --- Conclusion --- p.115 / Bibliography --- p.118

by Cai, Chun Hing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 109-114). / Abstract also in Chinese. / Acknowledgments --- p.ii / Abstract --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Main Categories in Data Mining --- p.1 / Chapter 1.2 --- Motivation --- p.3 / Chapter 1.3 --- Problem Definition --- p.4 / Chapter 1.4 --- Experimental Setup --- p.5 / Chapter 1.5 --- Outline of the thesis --- p.6 / Chapter 2 --- Literature Survey on Data Mining --- p.8 / Chapter 2.1 --- Statistical Approach --- p.8 / Chapter 2.1.1 --- Statistical Modeling --- p.9 / Chapter 2.1.2 --- Hypothesis testing --- p.10 / Chapter 2.1.3 --- Robustness and Outliers --- p.11 / Chapter 2.1.4 --- Sampling --- p.12 / Chapter 2.1.5 --- Correlation --- p.15 / Chapter 2.1.6 --- Quality Control --- p.16 / Chapter 2.2 --- Artificial Intelligence Approach --- p.18 / Chapter 2.2.1 --- Bayesian Network --- p.19 / Chapter 2.2.2 --- Decision Tree Approach --- p.20 / Chapter 2.2.3 --- Rough Set Approach --- p.21 / Chapter 2.3 --- Database-oriented Approach --- p.23 / Chapter 2.3.1 --- Characteristic and Classification Rules --- p.23 / Chapter 2.3.2 --- Association Rules --- p.24 / Chapter 3 --- Background --- p.27 / Chapter 3.1 --- Iterative Procedure: Apriori Gen --- p.27 / Chapter 3.1.1 --- Binary association rules --- p.27 / Chapter 3.1.2 --- Apriori Gen --- p.29 / Chapter 3.1.3 --- Closure Properties --- p.30 / Chapter 3.2 --- Introduction of Weights --- p.31 / Chapter 3.2.1 --- Motivation --- p.31 / Chapter 3.3 --- Summary --- p.32 / Chapter 4 --- Mining weighted binary association rules --- p.33 / Chapter 4.1 --- Introduction of binary weighted association rules --- p.33 / Chapter 4.2 --- Weighted Binary Association Rules --- p.34 / Chapter 4.2.1 --- Introduction --- p.34 / Chapter 4.2.2 --- Motivation behind weights and counts --- p.36 / Chapter 4.2.3 --- K-support bounds --- p.37 / Chapter 4.2.4 --- Algorithm for Mining Weighted Association Rules --- p.38 / Chapter 4.3 --- Mining Normalized Weighted association rules --- p.43 / Chapter 4.3.1 --- Another approach for normalized weighted case --- p.45 / Chapter 4.3.2 --- Algorithm for Mining Normalized Weighted Association Rules --- p.46 / Chapter 4.4 --- Performance Study --- p.49 / Chapter 4.4.1 --- Performance Evaluation on the Synthetic Database --- p.49 / Chapter 4.4.2 --- Performance Evaluation on the Real Database --- p.58 / Chapter 4.5 --- Discussion --- p.65 / Chapter 4.6 --- Summary --- p.66 / Chapter 5 --- Mining Fuzzy Weighted Association Rules --- p.67 / Chapter 5.1 --- Introduction to the Fuzzy Rules --- p.67 / Chapter 5.2 --- Weighted Fuzzy Association Rules --- p.69 / Chapter 5.2.1 --- Problem Definition --- p.69 / Chapter 5.2.2 --- Introduction of Weights --- p.71 / Chapter 5.2.3 --- K-bound --- p.73 / Chapter 5.2.4 --- Algorithm for Mining Fuzzy Association Rules for Weighted Items --- p.74 / Chapter 5.3 --- Performance Evaluation --- p.77 / Chapter 5.3.1 --- Performance of the algorithm --- p.77 / Chapter 5.3.2 --- Comparison of unweighted and weighted case --- p.79 / Chapter 5.4 --- Note on the implementation details --- p.81 / Chapter 5.5 --- Summary --- p.81 / Chapter 6 --- Mining weighted association rules with sampling --- p.83 / Chapter 6.1 --- Introduction --- p.83 / Chapter 6.2 --- Sampling Procedures --- p.84 / Chapter 6.2.1 --- Sampling technique --- p.84 / Chapter 6.2.2 --- Algorithm for Mining Weighted Association Rules with Sampling --- p.86 / Chapter 6.3 --- Performance Study --- p.88 / Chapter 6.4 --- Discussion --- p.91 / Chapter 6.5 --- Summary --- p.91 / Chapter 7 --- Database Maintenance with Quality Control method --- p.92 / Chapter 7.1 --- Introduction --- p.92 / Chapter 7.1.1 --- Motivation of using the quality control method --- p.93 / Chapter 7.2 --- Quality Control Method --- p.94 / Chapter 7.2.1 --- Motivation of using Mil. Std. 105D --- p.95 / Chapter 7.2.2 --- Military Standard 105D Procedure [12] --- p.95 / Chapter 7.3 --- Mapping the Database Maintenance to the Quality Control --- p.96 / Chapter 7.3.1 --- Algorithm for Database Maintenance --- p.98 / Chapter 7.4 --- Performance Evaluation --- p.102 / Chapter 7.5 --- Discussion --- p.104 / Chapter 7.6 --- Summary --- p.105 / Chapter 8 --- Conclusion and Future Work --- p.106 / Chapter 8.1 --- Summary of the Thesis --- p.106 / Chapter 8.2 --- Conclusions --- p.107 / Chapter 8.3 --- Future Work --- p.108 / Bibliography --- p.108 / Appendix --- p.115 / Chapter A --- Generating a random number --- p.115 / Chapter B --- Hypergeometric distribution --- p.116 / Chapter C --- Quality control tables --- p.117 / Chapter D --- Rules extracted from the database --- p.120

Lee Shuk Yee. / "October 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 87-92) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Inherent safety has been recognized as a design approach useful to remove or reduce hazards at the source instead of controlling them with add-on protective barriers. However, inherent safety is based on qualitative principles that cannot easily be evaluated and analyzed, and this is one of the major difficulties for the systematic application and quantification of inherent safety in plant design. The present research introduces the use of fuzzy logic for the measurement of inherent safety by proposing a hierarchical fuzzy model. This dissertation establishes a novel conceptual framework for the analysis of inherent safety and proposes a methodology that addresses several of the limitations of the methodologies available for current inherent safety analysis. This research proposes a methodology based on a hierarchical fuzzy model that analyzes the interaction of variables relevant for inherent safety and process safety in general. The use of fuzzy logic is helpful for modeling uncertainty and subjectivities implied in evaluation of certain variables and it is helpful for combining quantitative data with qualitative information. Fuzzy logic offers the advantage of being able to model numerical and heuristic expert knowledge by using fuzzy IF-THEN rules. Safety is traditionally considered a subjective issue because of the high uncertainty associated with its significant descriptors and parameters; however, this research recognizes that rather than subjective, "safety" is a vague problem. Vagueness derives from the fact that it is not possible to define sharp boundaries between safe and unsafe states; therefore the problem is a "matter of degree". The proposed method is computer-based and process simulator-oriented in order to reduce the time and expertise required for the analysis. It is expected that in the future, by linking the present approach to a process simulator, process engineers can develop safety analysis during the early stages of the design in a rapid and systematic way. Another important aspect of inherent safety, rarely addressed, is transportation of chemical substances; this dissertation includes the analysis of transportation hazard by truck using a fuzzy logic-based approach.

In a more competitive environment, load forecast serves two different applications. First, load forecast results can be used by the retailers of power to study their opportunities and plan their business strategies. Second, accurate projections of load are useful for T&D operators in performing system operation and expansion studies. Several key elements in their market and system planning studies have strong location factors that the spatial load forecast can address. In this dissertation, a package that integrates a Geographic Information System (GIS) used for automatic mapping and facility management (AM/FM) and a spatial load forecast module is presented. The interface functions and the procedure of the fuzzy logic based spatial load forecast module are described. Simulation studies are performed on a metropolitan area of Kaohsiung, Taiwan. The conventional fuzzy modeling has a drawback in that the fuzzy rules or the fuzzy membership functions are determined by trial and error. In this dissertation an automatic model identification procedure is proposed to construct the fuzzy model for short-term load forecast. In this method an analysis of variance is used to identify the influential variables on the system load. To setup the fuzzy rules, a cluster estimation method is adopted to determine the number of rules and the membership functions of variables involved in the premises of the rules. A recursive least square method is then used to determine the coefficients in the conclusion parts of the rules. None of these steps involves nonlinear optimization and all steps have well-bounded computation time.

Inherent safety has been recognized as a design approach useful to remove or reduce hazards at the source instead of controlling them with add-on protective barriers. However, inherent safety is based on qualitative principles that cannot easily be evaluated and analyzed, and this is one of the major difficulties for the systematic application and quantification of inherent safety in plant design. The present research introduces the use of fuzzy logic for the measurement of inherent safety by proposing a hierarchical fuzzy model. This dissertation establishes a novel conceptual framework for the analysis of inherent safety and proposes a methodology that addresses several of the limitations of the methodologies available for current inherent safety analysis. This research proposes a methodology based on a hierarchical fuzzy model that analyzes the interaction of variables relevant for inherent safety and process safety in general. The use of fuzzy logic is helpful for modeling uncertainty and subjectivities implied in evaluation of certain variables and it is helpful for combining quantitative data with qualitative information. Fuzzy logic offers the advantage of being able to model numerical and heuristic expert knowledge by using fuzzy IF-THEN rules. Safety is traditionally considered a subjective issue because of the high uncertainty associated with its significant descriptors and parameters; however, this research recognizes that rather than subjective, "safety" is a vague problem. Vagueness derives from the fact that it is not possible to define sharp boundaries between safe and unsafe states; therefore the problem is a "matter of degree". The proposed method is computer-based and process simulator-oriented in order to reduce the time and expertise required for the analysis. It is expected that in the future, by linking the present approach to a process simulator, process engineers can develop safety analysis during the early stages of the design in a rapid and systematic way. Another important aspect of inherent safety, rarely addressed, is transportation of chemical substances; this dissertation includes the analysis of transportation hazard by truck using a fuzzy logic-based approach.

Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains xiii, 252 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 250-251).

Zugl.: Kassel, Univ., Diss., 2009

Universiẗat - Herdecke, Witten, Thesis (doctoral).