M.Ing. (Electrical & Electronic Engineering) / Please refer to full text to view abstract

M.Ing. (Electrical & Electronic Engineering) / A dynamic process is considered modelled and identified when the model can predict its future behaviour as a result of a known stimulus. However, practical reality is complex and it is quite difficult to totally encompass a model representing a physical phenomenon in a mathematical formulation. Besides, to keep such formulations tractable, certain restrictive assumptions such as, for example, linearity, are often required. The common feature of general control-theoretic methods used for modelling is that they presuppose the valid and accurate knowledge of the processes to be controlled. If, however, one does not understand the inner workings of a complex process that one wishes to model, traditional techniques rarely yield satisfactory results. As systems become more complex it becomes increasingly difficult to make mathematical statements about them which are both meaningful and precise. Thus one is compelled to concede that imprecision and inexactness must be accepted in any real system application. The theory of fuzzy sets is a methodology for the handling of qualitative, inexact, imprecise, information in a systematic and rigorous way. This approach provides an excellent tool for the modelling of human-centered systems, especially because fuzziness seems to be an important facet of the human thinking process. Instead of using a precisely defined or measured value of a variable, a human being tends to summarize available information by classifying into vague and imprecise categories such as, for example, low, medium, high. In this way, the information received from the outside world is reduced to just what is needed to perform the task on hand with the required precision. Thus there is no need for precise mathematical models and thereby the human (i.e. fuzzy) decision-making mechanism has considerably less computational overhead and is thus faster and more conducive to biological survival than an equivalent precise mathematical model...

Orientadores: Cloris Perin Filho, PaulVinhas Ribeiro / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade Estadual de Engenharia Eletrica / Made available in DSpace on 2018-07-19T03:36:05Z (GMT). No. of bitstreams: 1 Cunha_DarliPalma_M.pdf: 4850380 bytes, checksum: f57f8fa398dec9515c3d3229b1f3765f (MD5) Previous issue date: 1993 / Resumo: Este trabalho apresenta as bases teóricas da Teoria dos Conjuntos Nebulosos proposta como uma teoria matemática capaz de expressar fenômenos vagos e incertos. Esta teoria é aplicada em um problema particular de Fluxos em Rede: o Problema de Fluxo Máximo em uma rede capacitada onde as informações disponiveis sobre as restrições de capacidade da rede são imprecisas. Testam-se três operadores de agregação de conjuntos nebulosos para duas redes onde se deseja determinar o fluxo máximo nebuloso. Um modelo para o planejamento de médio prazo do escoamento de produção de laranja para a produção de suco que utiliza o algoritmo de Fluxo Máximo nebuloso é apresentado. Neste modelo se deseja obter o dimensionamento da frota de caminhões de frete a ser contratada / Abstract: Not informed. / Mestrado / Mestre em Engenharia Elétrica

No description available.

Wong Man-Lung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 99-103). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / List of Figures --- p.viii / List of Tables --- p.ix / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Objectives --- p.5 / Chapter 2 --- Cartesian Representation of Membership Function --- p.7 / Chapter 2.1 --- The Cartesian Representation --- p.8 / Chapter 2.2 --- Region of Well-defined Membership Functions --- p.10 / Chapter 2.3 --- Similarity Triangle Interpolation Method --- p.12 / Chapter 2.4 --- The Interpolation Example --- p.18 / Chapter 2.5 --- Further Issues --- p.23 / Chapter 2.6 --- Conclusions --- p.24 / Chapter 3 --- Membership Function as Elements in Function Space --- p.26 / Chapter 3.1 --- L2[0，2] Representation --- p.27 / Chapter 3.2 --- "The Inner Product Space of L2[0,2]" --- p.31 / Chapter 3.3 --- The Similarity Triangle Interpolation Method --- p.32 / Chapter 3.4 --- The Interpolation Example --- p.36 / Chapter 3.5 --- Conclusions --- p.48 / Chapter 4 --- Radius of Influence of Membership Functions --- p.50 / Chapter 4.1 --- Previous Works on Mountain Method --- p.51 / Chapter 4.2 --- Combining Mountain Method and Cartesian Representation --- p.56 / Chapter 4.3 --- Extensibility Function and Weighted-Sum-Averaging Equation --- p.61 / Chapter 4.4 --- Radius of Influence --- p.62 / Chapter 4.5 --- Combining Radius of Influence and Fuzzy Interpolation Technique --- p.64 / Chapter 4.6 --- Model Identification Example --- p.66 / Chapter 4.7 --- Eliminative Extraction --- p.67 / Chapter 4.8 --- Eliminative Extraction Example --- p.70 / Chapter 4.9 --- Conclusions --- p.71 / Chapter 5 --- Fuzzy Inferencing --- p.73 / Chapter 5.1 --- Fuzzy Inferencing and Interpolation in Cartesian Representation --- p.74 / Chapter 5.2 --- Sparse Rule Extraction via Radius of Influence and Elimination --- p.77 / Chapter 5.3 --- Single Input and Single Output Case --- p.78 / Chapter 5.4 --- Multiple Input and Single Output Case --- p.81 / Chapter 5.5 --- Application --- p.89 / Chapter 5.6 --- Conclusions --- p.94 / Chapter 6 --- Conclusions --- p.96 / Appendix --- p.99 / Bibliography --- p.99

Thesis (M.S.)--Ohio University, March, 1994. / Title from PDF t.p.

Thesis (Ph. D.)--University of Hong Kong, 1998. / Includes bibliographical references (leaves 234-242).

Orientadores: Rodney C. Bassanezi, Laecio C. Barros / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-03T19:12:03Z (GMT). No. of bitstreams: 1 Mizukoshi_MarinaTuyako_D.pdf: 826229 bytes, checksum: bab9a94373928b4b6b18c1046ebc22c6 (MD5) Previous issue date: 2004 / Doutorado / Doutor em Matemática

In this thesis we first extend the notion of fuzzy normality to the notion of normality of a fuzzy subgroup in another fuzzy group. This leads to the study of normal series of fuzzy subgroups, and this study includes solvable and nilpotent fuzzy groups, and the fuzzy version of the Jordan-Hõlder Theorem. Furthermore we use the notion of normality to study products and direct products of fuzzy subgroups. We present a notion of fuzzy isomorphism which enables us to state and prove the three well-known isomorphism theorems and the fact that the internal direct product of two normal fuzzy subgroups is isomorphic to the external direct product of the same fuzzy subgroups. A brief discussion on fuzzy subgroups generated by fuzzy subsets is also presented, and this leads to the fuzzy version of the Basis Theorem. Finally, the notion of direct product enables us to study decomposable and indecomposable fuzzy subgroups, and this study includes the fuzzy version of the Remak-Krull-Schmidt Theorem.

Master’s thesis deals with mathematical operation with fuzzy numbers. The first part of the thesis deals with theoretical knowledge of fuzzy arithmetic and defines fuzzy sets, fuzzy numbers, universum and five membership function used in program. In the concrete it describes – cut method for dealing with fuzzy numbers as with limited interval for specific level which simplifies computation. The second part of the thesis contains description of programmed module for mathematical operation with fuzzy numbers. There is described creation of user interface which is using to set parameters of computation. There are also described support functions which make operation with fuzzy numbers possible and operation ensures output.