We propose a novel approach for building a type-2 neuro-fuzzy system from a given set of input-output training data. For an input pattern, a corresponding crisp output of the system is obtained by combining the inferred results of all the rules into a type-2 fuzzy set which is then defuzzified by applying a type reduction algorithm. Karnik and Mendel proposed an algorithm, called KM algorithm, to compute the centroid of an interval type-2 fuzzy set efficiently. Based on this algorithm, Liu developed a centroid type-reduction strategy to do type reduction for type-2 fuzzy sets. A type-2 fuzzy set is decomposed into a collection of interval type-2 fuzzy sets by £\-cuts. Then the KM algorithm is called for each interval type-2 fuzzy set iteratively. However, the initialization of the switch point in each application of the KM algorithm is not a good one. In this thesis, we present an improvement to Liu's algorithm. We employ the result previously obtained to construct the starting values in the current application of the KM algorithm. Convergence in each iteration except the first one can then speed up and type reduction for type-2 fuzzy sets can be done faster. The efficiency of the improved algorithm is analyzed mathematically and demonstrated by experimental results. Constructing a type-2 neuro-fuzzy system involves two major phases, structure identification and parameter identification. We propose a method which incorporates self-constructing fuzzy clustering algorithm and a SVD-based least squares estimator for structure identification of type-2 neuro-fuzzy modeling. The self-constructing fuzzy clustering method is used to partition the training data set into clusters through input-similarity and output-similarity tests. The membership function associated with each cluster is defined with the mean and deviation of the data points included in the cluster. Then applying SVD-based least squares estimator, a type-2 fuzzy TSK IF-THEN rule is derived from each cluster to form a fuzzy rule base. After that a fuzzy neural network is constructed. In the parameter identification phase, the parameters associated with the rules are then refined through learning. We propose a hybrid learning algorithm which incorporates particle swarm optimization and a SVD-based least squares estimator to refine the antecedent parameters and the consequent parameters, respectively. We demonstrate the effectiveness of our proposed approach in constructing type-2 neuro-fuzzy systems by showing the results for two nonlinear functions and two real-world benchmark datasets. Besides, we use the proposed approach to construct a type-2 neuro-fuzzy system to forecast the daily Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). Experimental results show that our forecasting system performs better than other methods.

Spatial Decision Making as a branch of decision making science deals with geographically related data in order to achieve complex spatial decision problems. Fuzzy set theory is one of the methods that can be used to come up with these types of problems. On the other hand, Geographical Information Systems (GIS) is one of the most powerful tools that we can use to accomplish spatial decision problems. Selection of the suitable site or land-use for the real estate is also a spatial decision making problem. When we consider the initial dynamics of the suitably located property from the point of view of value and potential we observe that the &ldquo / good location&rdquo / is the dominating factor. This study reports on the development of a kind of decision support system for locational suitability of settlements that integrates the fuzzy set (FZ) theory, a rule-based system (RBS) and GIS. This study is thought as the assistant for the property managers that are buyers and sellers. It can function as the property consultant for the buyers when they are looking for a property to buy and also it helps the real estate agencies to sell their properties. On the other hand, different scenarios of the potential areas according to the different user&rsquo / s preferences are depicted and they are joined and compared with the results of the vulnerability to earthquake hazards&rsquo / of the same area. Odunpazari - Eskisehir area is selected for implementation of the case study because of the data availability. As a result of this study, it can be said that most suitable property changes depending on the people&rsquo / s preferences. In addition, it is seen that most of the buildings that are locationally suitable are highly vulnerable to the earthquake hazards.

Notre thèse s'inscrit dans le cadre de la mise en place d'un observatoire des pratiques agricoles dans le bassin versant de la Vesle. L'objectif de ce système d'information agri-environnemental est de comprendre les pratiques responsables de la pollution de la ressource en eau par les pesticides d'origine agricole sur le territoire étudié et de fournir des outils pertinents et pérennes pour estimer leurs impacts. Notre problématique concerne la prise en compte de l'imperfection dans le processus de la fusion de données multi-sources et imparfaites. En effet, l'information sur les pratiques n'est pas exhaustive et ne fait pas l'objet d'une déclaration, il nous faut donc construire cette connaissance par l'utilisation conjointe de sources multiples et de qualités diverses en intégrant dans le système d'information la gestion de l'information imparfaite. Dans ce contexte, nous proposons des méthodes pour une reconstruction spatialisée des informations liées aux pratiques agricoles à partir de la télédétection, du RPG, d'enquêtes terrain et de dires d'experts, reconstruction qualifiée par une évaluation de la qualité de l'information. Par ailleurs, nous proposons une modélisation conceptuelle des entités agronomiques imparfaites du système d'information en nous appuyant sur UML et PERCEPTORY. Nous proposons ainsi des modèles de représentation de l'information imparfaite issues des différentes sources d'information à l'aide soit des ensembles flous, soit de la théorie des fonctions de croyance et nous intégrons ces modèles dans le calcul d'indicateurs agri-environnementaux tels que l'IFT et le QSA. / Our thesis is part of a regional project aiming the development of a community environmental information system for agricultural practices in the watershed of the Vesle. The objective of this observatory is 1) to understand the practices of responsible of the water resource pollution by pesticides from agriculture in the study area and 2) to provide relevant and sustainable tools to estimate their impacts. Our open issue deals with the consideration of imperfection in the process of merging multiple sources and imperfect data. Indeed, information on practices is not exhaustive and is not subject to return, so we need to build this knowledge through the combination of multiple sources and of varying quality by integrating imperfect information management information in the system. In this context, we propose methods for spatial reconstruction of information related to agricultural practices from the RPG remote sensing, field surveys and expert opinions, skilled reconstruction with an assessment of the quality of the information. Furthermore, we propose a conceptual modeling of agronomic entities' imperfect information system building on UML and PERCEPTORY.We provide tools and models of representation of imperfect information from the various sources of information using fuzzy sets and the belief function theory and integrate these models into the computation of agri-environmental indicators such as TFI and ASQ.

Face à l'augmentation significative du taux de mortalité par cancer du sein chez les femmes ainsi que la croissance continue du nombre de mammographies réalisées chaque année, le diagnostic assisté par ordinateur devient de plus en plus impératif pour les experts. Dans notre travail de thèse, une attention particulière est accordée aux masses mammaires vu qu'elles représentent le signe de cancer du sein le plus couramment observé en mammographies. Néanmoins, ces images présentent un très faible contraste, ce qui fait que les frontières entre les tissus sains et les masses sont mal définies. C'est ainsi qu'il est difficile de pouvoir discerner avec précision ces masses et de leur définir un contour unique. En outre, la complexité et la grande variabilité des formes des masses mammaires rendent les tâches de diagnostic et de classification difficiles. Dans ce cadre, nous proposons un système d'aide au diagnostic dont le but est la segmentation de masses dans les régions d'intérêt et par la suite la classification de ces masses en deux catégories : bénignes et malignes. La première étape de segmentation est une étape assez délicate vu que les étapes postérieures à savoir la caractérisation et la classification y sont dépendantes. En effet, une mauvaise segmentation peut entrainer une mauvaise prise de décision. Un tel cas peut survenir en raison de l'incertitude et l'imprécision émanant de l'image mammographique. C'est pour cette raison que nous proposons une définition de contours flous permettant de prendre en compte ces types d'imperfections. Ces contours flous sont introduits dans l'énergie d'un contour actif pour modifier son mouvement et aboutir à une délimitation exacte des masses. Une fois les régions d'intérêt sont segmentées, nous présentons une méthode de classification de masses basée sur la théorie des possibilités qui permet de modéliser les ambigüités inhérentes aux connaissances exprimées par l'expert. En outre, cette méthode utilise essentiellement les descripteurs de forme pour caractériser les masses et décider de leur degré de gravité vu que la forme des masses constitue un bon indicateur de gravité.La validation et l'évaluation de ces deux méthodes sont réalisées en utilisant les régions d'intérêt contenant des masses extraites de la base MIAS. Les résultats obtenus sont très intéressants et les comparaisons effectuées ont mis en évidence leurs performances. / In view of the significant increase in breast cancer mortality rate among women as well as the continuous growth in number of mammograms performed each year, computer-aided diagnosis is becoming more and more imperative for experts. In our thesis work, special attention is given to breast masses as they represent the most common sign of breast cancer in mammograms. Nevertheless, mammographic images have very low contrast and breast masses possess ambiguous margins. Thus, it is difficult to distinguish them from the surrounding parenchymal. Moreover, the complexity and the large variability of breast mass shapes make diagnostic and classification challenging tasks.In this context, we propose a computer-aided diagnosis system which firstly segments masses in regions of interests and then classifies them as benign or malignant. Mass segmentation is a critical step in a computer-aided diagnosis system since it affects the performance of subsequent analysis steps namely feature analysis and classification. Indeed, poor segmentation may lead to poor decision making. Such a case may occur due to two types of imperfection: uncertainty and imprecision. Therefore, we propose to deal with these imperfections using fuzzy contours which are integrated in the energy of an active contour to get a fuzzy-energy based active contour model that is used for final delineation of mass.After mass segmentation, a classification method is proposed. This method is based on possibility theory which allows modeling the ambiguities inherent to the knowledge expressed by the expert. Moreover, since shape and margin characteristics are very important for differentiating between benign and malignant masses, the proposed method is essentially based on shape descriptors.The evaluation of the proposed methods was carried out using the regions of interest containing masses extracted from the MIAS base. The obtained results are very interesting and the comparisons made have demonstrated their performances.

The theme of the thesis is to make a list few fundamental modern methods which can be used in computerized physics. The thesis describes parallel computing, neural networks,genetic algorithms, fuzzy logic. Every chapter include theoretical description, simplified mathematical expression, proposals of technical solution. Applications are briefly mentioned here too. The printed matter is completed with a few simple examples. The closing part of the thesis acquired information about these methods and outlines their future development.

Made available in DSpace on 2014-12-17T14:34:45Z (GMT). No. of bitstreams: 1 JanainaO_DISSERT.pdf: 1740666 bytes, checksum: b7ce8fa758db532c2ba4cf361ed5d2a2 (MD5) Previous issue date: 2013-06-03 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Pobreza significa priva??o. A priva??o sofrida pelas pessoas pobres normalmente est? relacionada ao baixo n?vel de renda. Quando se trata da pobreza no Brasil, a Regi?o Nordeste se destaca, pois em 2010 o Plano Brasil Sem Mis?ria apontou a exist?ncia de 9,6 milh?es de extremamente pobres na regi?o, representando 59% do total no pa?s (BRASIL, 2011b). No entanto, a renda monet?ria n?o captura completamente as priva??es sofridas pelas pessoas. O conceito de pobreza tem passado por uma evolu??o no sentido de incluir dimens?es importantes sobre a vida das pessoas. Deixou de focar apenas a priva??o absoluta da abordagem unidimensional e passou a considerar a priva??o relativa, uma abordagem multidimensional. Este estudo fundamenta-se na Abordagem das Capacita??es de Sen (1981, 1985, 2000, 2001) que considera a pobreza como a priva??o sofrida pelas pessoas relacionada a diferentes aspectos tais como nutri??o, acesso aos servi?os b?sicos de educa??o, sa?de, saneamento b?sico e, tamb?m, de liberdade. Assim, o objetivo deste estudo ? investigar e mensurar a intensidade da pobreza multidimensional nos munic?pios da Regi?o Nordeste atrav?s do Censo Demogr?fico (IBGE, 2010). Para isso, utilizou-se da t?cnica da Teoria dos Conjuntos Fuzzy que permite o c?lculo de ?ndice relativo. A mensura??o da pobreza por meio do ?ndice fuzzy de pobreza (IFP) envolveu a escolha de 19 indicadores distribu?dos em quatro dimens?es (ou capacita??es): educa??o, sa?de, condi??es habitacionais e renda. Os resultados mostraram que existe maior pobreza na dimens?o renda. Entretanto, as dimens?es educa??o e sa?de tamb?m tiveram import?ncia no indicador de pobreza multidimensional. Alguns indicadores que merecem aten??o dos formuladores de pol?ticas p?blicas s?o o acesso ao ensino fundamental e ensino m?dio e o acesso aos servi?os de saneamento b?sico, coleta de lixo e rede de ?gua. Apesar da priva??o na dimens?o renda ser maior do que nas demais dimens?es, a supera??o da pobreza envolve a promo??o dos diferentes aspectos relacionados ? vida das pessoas. A Abordagem da Capacita??o mostra que pol?ticas de assist?ncia aos pobres precisam considerar as particularidades do local e h?bitos, de forma a verificar quais s?o as reais priva??es sofridas pelas pessoas. As pessoas que se encontram em situa??o de pobreza precisam ser incentivadas a superar a situa??o de mis?ria e pen?ria de forma a n?o serem eternamente privadas de liberdade e privadas de expandirem suas capacita??es

Atualmente, o ambiente industrial é caracterizado pela intensa globalização, competição entre cadeias de suprimentos, manutenção das competências centrais e terceirização dos demais serviços. Desta forma, a gestão das relações entre os agentes independentes da cadeia de suprimentos e do processo de aquisição são fatores potenciais para o aumento da competitividade empresarial. No contexto da construção civil, a seleção adequada dos parceiros de negócios é um elemento fundamental para o sucesso dos projetos, uma vez que uma grande proporção das atividades podem ser sub-contratadas e possuem relação de precedência entre si. Os suprimentos representam um percentual significativo dos custos das construções, 60%, dado que demonstra o potencial de lucratividade passível de ser atingida ao estruturar o processo de seleção de fornecedores na construção civil. Seleções baseadas no preço prejudicam os sub-empreiteiros e fornecedores mais responsáveis na concorrência, contribuindo para a queda do nível de desempenho e redução da eficiência global do projeto, uma vez que as ineficiências são somadas ao longo da cadeia. Através da estruturação do processo de seleção de fornecedores, é possível mitigar os riscos de suprimentos oriundos de falhas destes contratados ao longo da relação. O objetivo deste trabalho foi desenvolver uma sistemática para seleção de fornecedores críticos, considerando diversos critérios além do preço, entre qualitativos e quantitativos. A abordagem visa também, a eliminação da subjetividade do processo e a extração do melhor fornecedor de forma objetiva. Para tanto, foram definidas dimensões competitivas para avaliar os fornecedores e posteriormente foram utilizados dois métodos quantitativos, Teoria dos Conjuntos Difusos (TCD) e Análise de Componentes Principais (ACP), para selecionar o melhor fornecedor dentre as alternativas, com base na avaliação de múltiplos agentes. / Currently, the industrial environment is characterized by intense globalization, competition between supply chains, maintenance of core competencies and outsourcing of other services. Thus, the management of relationships between independent agents of the supply chain and the procurement process are potential factors for increasing enterprise competitiveness. In the construction context, the proper selection of business partners is a key element for the success of projects, since a large proportion of the activities can be sub-contracted and have precedence relationship between them. Supplies represent a significant percentage of the cost of buildings, 60%, information that demonstrates the potential of profitability that can be achieved by structuring the process of supplier selection in the construction industry. Selection based on price take off from competition the sub-contractors and suppliers more responsible, contributing to the decline in the level of performance and reduction in the overall project efficiency, since inefficiencies are summed through the chain. By structuring the supplier selection process, it is possible to mitigate the supply risk arising from failures of these suppliers during the relationship. The objective of this study was to develop a systematic for selection of critical suppliers, considering several criteria other than price, among qualitative and quantitative. The approach also aims at eliminating the subjectivity of the process and the extraction of the best supplier in an objective way. In order to that, competitive dimensions were set to evaluate vendors and subsequently two quantitative methods, Fuzzy Sets Theory (FST) and Principal Component Analysis (PCA) were used to select the best supplier among the alternatives based on multiple agents evaluation.

Orientador: Jurandir Zullo Junior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-05T08:07:53Z (GMT). No. of bitstreams: 1 Antunes_JoaoFranciscoGoncalves_M.pdf: 7524504 bytes, checksum: e36a3c933615dc4ef031bf119f6c09ff (MD5) Previous issue date: 2005 / Resumo: A estimativa precisa com antecedência à época da colheita de áreas plantadas com culturas agrícolas, como a soja, é de fundamental importância para a economia brasileira. A previsão do escoamento e comercialização da produção agrícola é estratégica para o Brasil, pois estão diretamente relacionados com o planejamento, custos e preço. Com o recente avanço tecnológico na obtenção de dados por sensoriamento remoto orbital é possível melhorar a previsão de safras, diminuindo cada vez mais o nível de subjetividade. Embora designadas para fins meteorológicos, as imagens AVHRR-NOAA de elevada repetitividade temporal, têm sido utilizadas para o monitoramento agrícola. Porém, a sua baixa resolução espacial faz com que possa ocorrer a mistura espectral das classes de cobertura do solo dentro de um mesmo pixel e isso pode acarretar problemas de imprecisão na estimativa de área plantada de uma cultura agrícola. O objetivo principal do trabalho foi desenvolver uma metodologia de classificação automática com a aplicação de lógica fuzzy para o reconhecimento de padrões em imagens AVHRR-NOAA, utilizando índices de vegetação para estimar a área plantada de soja no nível sub-pixel. Para oito municípios produtores de soja da região oeste do Estado do Paraná, foi possível obter a estimativa de área no final de janeiro de 2004, com antecedência em relação à época da colheita, ao contrário dos levantamentos oficiais que se estendem até o final da safra, além de utilizarem dados subjetivos vindos do campo. As estimativas de área de soja baseadas em classificação fuzzy mostraram-se altamente correlacionadas com as estimativas de área de referência obtidas a partir da máscara de soja e por expansão direta, sendo um indicativo de boa precisão. E também apresentaram alta correlação, balizadas com as estimativas oficiais da SEAB/DERAL e do IBGE. Em ambas comparações, o nível de erro relativo geral foi aceitável. O sistema desenvolvido para processamento e geração de produtos das imagens AVHRR-NOAA mostrou-se uma ferramenta fundamental de infra-estrutura, por aliar automação e precisão a metodologia do trabalho / Abstract: An early accurate estimation of agricultural crop areas, such as soybean, is fundamental for the Brazilian economy. The draining forecast and the estimation of agricultural production commercialization are strategic to Brazil, since they are directly related to planning, costs and price. Recent technological progress of data acquisition from orbital remote sensing makes possible to improve harvest forecast, reducing more and more the level of subjectivity. Although designed for meteorological aims, the AVHRR-NOAA images of high temporal resolution, have been used for the crop monitoring. However, its low spatial resolution might cause the spectral mixture of the different land cover classes within the same pixel and it can lead to accuracy problems on crop area estimation. The main objective of the work was to develop an automatic classification methodology with the application of fuzzy logic for pattern recognition in AVHRR-NOAA images, using vegetation indices to estimate the soybean crop areas at sub-pixel level. For eight soybean producer counties in the West region of the Paraná State, it was possible to obtain the crop area estimation at the end of january 2004, prior to the harvest period, on the contrary of the official surveys that extend until the end of the harvest, besides using subjective data collected on the field. The soybean crop area estimation based on fuzzy classification showed to be highly correlated with the reference area estimation obtained from the soybean mask and by direct expansion, being an indicative of good accuracy. And also presented high correlation, marked out with the official estimations from SEAB/DERAL and IBGE. In both comparisons, the level of general relative error was acceptable. The system developed for processing and products generation of AVHRR-NOAA images had proved to be a fundamental infrastructure tool, due to its capacity to combine automation and accuracy to the work methodology / Mestrado / Planejamento e Desenvolvimento Rural Sustentável / Mestre em Engenharia Agrícola

Esta tese busca, de início, pesquisar comparativamente, com a abrangência possível, os métodos e processos pelos quais as empresas componentes do setor siderúrgico nacional avaliam - sob o ponto de vista da viabililidade - a tomada de suas decisões relativamente a seus peculiares e vultosos investimentos em equipamentos de tecnologia avançada de manufatura. Revisita primária e metodologicamente amplo repertório de métodos de avaliação consagrados pela prática e pela literatura, tendo como cenário o setor em referência representado pelas várias modalidades industriais que o suportam. A pesquisa é realizada com base no estudo de múltiplos casos realizados em grandes empresas siderúrgicas, a partir de visitas a suas instalações e entrevistas com seus especialistas de análise de viabilidade de projetos de investimento. Ao final, oferece modelo de avaliação a ser adotado complementarmente aos critérios utilizados pelo setor em referência, a partir das observações e discussões efetuadas em campo. / This thesis initially seeks to search, in a comparative basis and with the permissible largeness, the methods and procedures through which the companies comprising the national siderurgic sector appraise their decisions referring to their peculiar and huge investments in AMT equipments, under a feasibility point of view. A primary review is made, in a comparative way, of a large well known evaluation methods repertoire, in a background at which act the various concerned industrial modalities. The search is accomplished based on the study of multiple cases realized at big siderurgic companies, by visiting their installations and by interviewing their specialists in investment feasibility project analysis. Eventually offers an evaluation model to be adopted by this sector, based upon the observation and discussions made in the related field research.

To be successful in today's competitive market, service providers should look at user's satisfaction as a critical key. In order to gain a better understanding of customers' expectations, a proper evaluations which considers intrinsic characteristics of perceived quality of service is needed. Due to the subjective nature of quality, the vagueness of human judgment and the uncertainty about the degree of users' linguistic satisfaction, fuzziness is associated with quality of experience. Considering the capability of Fuzzy logic in dealing with imprecision and qualitative knowledge, it would be wise to apply it as a powerful mathematical tool for analyzing the quality of experience (QoE). This thesis proposes a fuzzy procedure to evaluate the quality of experience. In our proposed methodology, we provide a fuzzy relationship between QoE and Quality of Service (QoS) parameters. To identify this fuzzy relationship a new term called Fuzzi ed Opinion Score (FOS) representing a fuzzy quality scale is introduced. A fuzzy data mining method is applied to construct the required number of fuzzy sets. Then, the appropriate membership functions describing fuzzy sets are modeled and compared with each other. The proposed methodology will assist service providers for better decision-making and resource management.