La borrosidad como epistemología de la interacción social: algunos alcances y potencialidades

En este trabajo se describen la teoría de los conjuntos borrosos de L. A. Zadeh(antecedentes, características e implicaciones) y las áreas en las que se ha aplicado laborrosidad en psicología y psicología social (desarrollo evolutivo, procesamiento deestímulos, percepción de la información, prototipos y otras aplicaciones). A partir de esto,se sugiere cómo la borrosidad podría ser útil en el estudio de la interacción social,asumiendo el carácter simultáneamente vago y preciso de la realidad, y la utilización deconceptos como la noción de sí mismo desde una visión compleja, que considere, desde laperspectiva del pluralismo, diversas posturas teóricas y metodológicas.

Linguistic models for decision support

Linguistic modelling is a rather new branch of mathematics that is still undergoing rapid development. It is closely related to fuzzy set theory and fuzzy logic, but knowledge and experience from other fields of mathematics, as well as other fields of science including linguistics and behavioral sciences, is also necessary to build appropriate mathematical models. This topic has received considerable attention as it provides tools for mathematical representation of the most common means of human communication - natural language. Adding a natural language level to mathematical models can provide an interface between the mathematical representation of the modelled system and the user of the model - one that is sufficiently easy to use and understand, but yet conveys all the information necessary to avoid misinterpretations. It is, however, not a trivial task and the link between the linguistic and computational level of such models has to be established and maintained properly during the whole modelling process. In this thesis, we focus on the relationship between the linguistic and the mathematical level of decision support models. We discuss several important issues concerning the mathematical representation of meaning of linguistic expressions, their transformation into the language of mathematics and the retranslation of mathematical outputs back into natural language. In the first part of the thesis, our view of the linguistic modelling for decision support is presented and the main guidelines for building linguistic models for real-life decision support that are the basis of our modeling methodology are outlined. From the theoretical point of view, the issues of representation of meaning of linguistic terms, computations with these representations and the retranslation process back into the linguistic level (linguistic approximation) are studied in this part of the thesis. We focus on the reasonability of operations with the meanings of linguistic terms, the correspondence of the linguistic and mathematical level of the models and on proper presentation of appropriate outputs. We also discuss several issues concerning the ethical aspects of decision support - particularly the loss of meaning due to the transformation of mathematical outputs into natural language and the issue or responsibility for the final decisions. In the second part several case studies of real-life problems are presented. These provide background and necessary context and motivation for the mathematical results and models presented in this part. A linguistic decision support model for disaster management is presented here – formulated as a fuzzy linear programming problem and a heuristic solution to it is proposed. Uncertainty of outputs, expert knowledge concerning disaster response practice and the necessity of obtaining outputs that are easy to interpret (and available in very short time) are reflected in the design of the model. Saaty’s analytic hierarchy process (AHP) is considered in two case studies - first in the context of the evaluation of works of art, where a weak consistency condition is introduced and an adaptation of AHP for large matrices of preference intensities is presented. The second AHP case-study deals with the fuzzified version of AHP and its use for evaluation purposes – particularly the integration of peer-review into the evaluation of R&D outputs is considered. In the context of HR management, we present a fuzzy rule based evaluation model (academic faculty evaluation is considered) constructed to provide outputs that do not require linguistic approximation and are easily transformed into graphical information. This is achieved by designing a specific form of fuzzy inference. Finally the last case study is from the area of humanities - psychological diagnostics is considered and a linguistic fuzzy model for the interpretation of outputs of multidimensional questionnaires is suggested. The issue of the quality of data in mathematical classification models is also studied here. A modification of the receiver operating characteristics (ROC) method is presented to reflect variable quality of data instances in the validation set during classifier performance assessment. Twelve publications on which the author participated are appended as a third part of this thesis. These summarize the mathematical results and provide a closer insight into the issues of the practicalapplications that are considered in the second part of the thesis.

Union inclusiveness and temporary agency workers: the role of power resources and union ideology

This article investigates the determinants of union inclusiveness towards agency workers in Western Europe, using an index which combines unionization rates with dimensions of collective agreements covering agency workers. Using fuzzy-set Qualitative Comparative Analysis, we identify two combinations of conditions leading to inclusiveness: the ‘Northern path’ includes high union density, high bargaining coverage and high union authority, and is consistent with the power resources approach. The ‘Southern path’ combines high union authority, high bargaining coverage, statutory regulations of agency work and working-class orientation, showing that ideology rather than institutional incentives shapes union strategies towards the marginal workforce.

Generalized external indexes for comparing data partitions with overlapping categories

There is a family of well-known external clustering validity indexes to measure the degree of compatibility or similarity between two hard partitions of a given data set, including partitions with different numbers of categories. A unified, fully equivalent set-theoretic formulation for an important class of such indexes was derived and extended to the fuzzy domain in a previous work by the author [Campello, R.J.G.B., 2007. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Lett., 28, 833-841]. However, the proposed fuzzy set-theoretic formulation is not valid as a general approach for comparing two fuzzy partitions of data. Instead, it is an approach for comparing a fuzzy partition against a hard referential partition of the data into mutually disjoint categories. In this paper, generalized external indexes for comparing two data partitions with overlapping categories are introduced. These indexes can be used as general measures for comparing two partitions of the same data set into overlapping categories. An important issue that is seldom touched in the literature is also addressed in the paper, namely, how to compare two partitions of different subsamples of data. A number of pedagogical examples and three simulation experiments are presented and analyzed in details. A review of recent related work compiled from the literature is also provided. (c) 2010 Elsevier B.V. All rights reserved.

Estimativa da área impermeável dentro da bacia hidrográfica do Arroio Diluvio (porto Alegre/RS) através de técnicas de sensoriamento remoto e geoprocessamento

Neste trabalho foram realizadas classificações utilizando-se as bandas 1 a 5 e 7 dos sensores Landsat 5 TM (1987) e Landsat 7 ETM+ (2000). A caracterização espectral dos materiais foi realizada em laboratório utilizando um espectrorradiômetro, e através das bandas 1 a 5 e 7 dos sensores Landsat 5 TM (1987) e Landsat 7 ETM+ (2000). A transformação dos dados multiespectrais de imagens de sensoriamento remoto é uma maneira de reduzir o volume de dados através da identificação de classes de interesse numa imagem digital. No intuito de verificar condições de melhoramento na classificação de alvos urbanos em imagens digitais, identificados por procedimentos já conhecidos, como a classificação pela Máxima Verossimilhança, escolheu-se um classificador baseado na lógica fuzzy. O classificador utilizado foi o Fuzzy Set Membership classification - Fuzclass, que faz parte de um conjunto de classificadores não-rígidos disponíveis no programa Idrisi 32. Uma vez que informações sobre o desempenho de produtos deste classificador em áreas urbanas são escassas, foram conduzidos ensaios de comparação de resultados obtidos por este classificador com a verdade terrestre, representada por uma imagem de alta resolução espacial do satélite QuickBird. As áreas teste selecionadas desta imagem atendem ao critério de inalterância das condições de ocupação para o intervalo temporal considerado A comparação feita, permite concluir que o classificador apresenta limitações na classificação de áreas urbanas devido ao comportamento espectral semelhante dos materiais que fazem parte dessa cobertura. A utilização de uma classe única para identificar áreas impermeáveis foi a solução adotada para contornar este óbice. O emprego de áreas teste possibilitou acertar a escolha do grau de possibilidade de presença da classe no pixel (PPCP). Uma comparação entre os resultados apresentados na classificação de áreas impermeáveis, com base nos classificadores Máxima Verossimilhança e Fuzclass, demonstrou um desempenho melhor do classificador fuzzy, em função do nível de PPCP ajustado durante a análise comparativa Landsat e Quickbird nas áreas teste. Um procedimento alternativo de estimativa de áreas impermeáveis em bacias urbanas é apresentado no final.

Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem

This work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints are treated as objective function of the problem. This strategy allows attending the physical and operational restrictions without compromise the quality of the found solutions. The developed MOEA is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 test systems are presents to validate the efficiency of proposed model and solution technique.

On turbulent, erratic and other dynamical properties of Zadeh's extensions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Hardware implementation of type-2 programmable fuzzifier

The design of full programmable type-2 membership function circuit is presented in this paper. This circuit is used to implement the fuzzifier block of Type-2 Fuzzy Logic Controller chip. In this paper the type-2 fuzzy set was obtained by blurring the width of the type-1 fuzzy set. This circuit allows programming the height and the shape of the membership function. It operates in current mode, with supply voltage of 3.3V. The simulation results of interval type-2 membership function circuit have been done in CMOS 0.35μm technology using Mentor Graphics software. © 2011 IEEE.

Controle robusto de sistemas não-lineares sujeitos a falhas estruturais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Uma abordagem nebulosa de raciocínio baseado em casos para recomendação de sistemas marítimos de produção de petróleo

Pós-graduação em Ciência da Computação - IBILCE

Sistema eletrônico para geração e avaliação de movimentos em paraplégicos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Projeto de controladores baseados em LMIs: realimentação derivativa e sistemas chaveados utilizando estrutura variável

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Implementação de um sistema para geração e avaliação de movimentos em pacientes hemiplégicos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação de redes NeuroFuzzy ao processamento de peças automotivas por meio de injeção de polímeros

The injection molding of automotive parts is a complex process due to the many non-linear and multivariable phenomena that occur simultaneously. Commercial software applications exist for modeling the parameters of polymer injection but can be prohibitively expensive. It is possible to identify these parameters analytically, but applying classical theories of transport phenomena requires accurate information about the injection machine, product geometry, and process parameters. However, neurofuzzy networks, which achieve a synergy by combining the learning capabilities of an artificial neural network with a fuzzy set's inference mechanism, have shown success in this field. The purpose of this paper was to use a multilayer perceptron artificial neural network and a radial basis function artificial neural network combined with fuzzy sets to produce an inference mechanism that could predict injection mold cycle times. The results confirmed neurofuzzy networks as an effective alternative to solving such problems.