Dissertations / Theses on the topic 'Fuzzy anp'

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
The issue of sustainability, which is an increasingly important consideration in the day-to-day operations of businesses, needs to be addressed in the context of supplier selection. The use of multi-criteria approaches in decisions concerning the selection of sustainable suppliers can be a valuable approach for helping to resolve the complexity of this process. A systematic review of the literature highlighted gaps in the research in this area, such as the lack of a detailed description of multi-criteria methods, as well as a scarcity of sustainability criteria that take into account the three main perspectives (Economic, Environmental, and Social). This work describes the development and real case application of a DEMATEL (Decision-Making Trial and Evaluation Laboratory) and ANP (Analytic Network Process) fuzzy modeling technique for the selection of sustainable suppliers. The methodological approach used in the research was quantitative, descriptive, and empirical. The modeling and simulations were performed using MATLAB®. Incorporation of the specificities of the process of selection of sustainable suppliers makes the model a useful tool for use by both businesses and researchers concerned with the issue of sustainability. A systematic review of the published research highlights the main state-of-art methods and criteria used for the selection of green and sustainable suppliers. The model is described in a framework highlighting each step of the application. It was subsequently applied by a major glass packaging company, where the supply chain coordinator agreed to assist in the research. The outputs of the second phase of the model showed that the Economic cluster was most important and interacted with the Environmental cluster, while the Social cluster remained practically inert, without interactions with the other clusters. For the three perspectives, the three most influential criteria were as follows: cost, compliance, and quality (Economic perspective); environmental certifications/ISO 14001, reuse/recovery, and pollution control (Environmental perspective); and stakeholder rights, respect for policies, and encouragement of the development of self-sustainable recycling programs (Social perspective). Finally, a supplier was selected using the framework presented, and the criteria that most influenced the decision were highlighted. The procedure developed here offers a tool to assist businesses searching for sustainable solutions, as well as researchers in the scientific community concerned with the development of knowledge in this area.
A temática sustentável, cada vez mais presente e atuante no cotidiano das operações das empresas precisa ser tratada no contexto da seleção de fornecedores. Aplicar abordagens de decisão multicritério para a seleção de fornecedores sustentáveis demonstra ser uma alternativa interessante a fim de lidar com a natureza complexa deste processo. Por meio de uma revisão sistemática da literatura foi possível destacar algumas lacunas de pesquisa, como a falta de uma visão detalhada dos métodos multicritérios e uma escassa abordagem de critérios sustentáveis, abordando as três perspectivas (Econômica, Ambiental e Social). Desta forma, o objetivo central da pesquisa é detalhar e aplicar em um caso real a modelagem fuzzy DEMATEL (Decision-making Trial and Evaluation Laboratory) e fuzzy ANP (Analytic Network Process) para a seleção de fornecedores sustentáveis. A abordagem metodológica empregada na pesquisa é quantitativa descritiva empírica, com aplicação de modelagem e simulação em MATLAB ®. Espera-se que o modelo possa internalizar as especificidades do processo de seleção de fornecedores sustentáveis de modo a tornar-se uma ferramenta útil às empresas e aos pesquisadores que estudam o método. A revisão sistemática da pesquisa pode destacar os principais métodos na literatura e os principais critérios utilizados pela seleção de fornecedores verdes e sustentáveis no estado da arte. O modelo foi detalhado em um framework, destacando-se cada passo da aplicação. Posteriormente foi aplicado em uma empresa de grande porte de embalagens vítreas, onde a coordenadora de Supply Chain se dispôs a auxiliar na pesquisa. Como saídas da segunda fase do modelo, o cluster Econômico revelou ser o mais importante e exerce influência sob o cluster Ambiental, já o cluster Social permanece praticamente inerte sem exercer ou receber influência. Também pode-se destacar que os três critérios mais influentes de cada perspectiva foram: ‘Custo’, ‘Compliance’ e ‘Qualidade’ na perspectiva Econômica; ‘Certificações Ambientais / ISO 14001’, Reuso / Recuperação e ‘Controle da Poluição’ na perspectiva Ambiental; e ‘Direitos dos Stakeholders’, ‘Respeito pelas políticas’ e ‘Incentivo ao desenvolvimento de programas de reciclagem auto-sustentáveis’ na perspectiva Social. Ao final, um fornecedor é escolhido com o framework apresentado, e os critérios mais influentes na decisão foram destacados. Esta discussão é válida para auxiliar empresas em busca de soluções sustentáveis e pesquisadores na área que desenvolvem o conhecimento para a comunidade científica.

This thesis is concerned with problematics of multi-criteria decision making, especially method DEMATEL, its modifications and combinations with another methods. There is description of particular steps of this methods and selected theory from various areas, which is used in these methods. In every chapter we can find theoretical explanation of these methods, description of their steps and afterwards these theoretical findings are applied on numerical example. Firstly, the crucial method is introduced. Then this thesis is concerned with its adjustments based, on different approaches, which are often used not only in decision making. In the end, all these findings are aggregated and new method is founded.

The aim of this diploma thesis is refer to the supplier evaluation and selection process in business sphere. On the business sphere is putted emphasis because securing of company sources are more and more important parts of strategic decision making. In this paper is analyzed supplier selection problem and are analyzed most common methods to determine the best supplier. The goal of this paper is proposal of own program solution of evaluation and supplier selection build on evolution methods, specifically evolution algorithms. The part of this thesis is map of theoretical possibilities of evolution algorithms and implementation of program for solving supplier selection problem.

Дипломна робота: 90 с., 17 табл., 32 рис., 1 додаток, 23 джерела. Об’єкт дослідження: процеси вибору постачальника для потреб проекту. Предмет дослідження: методи та засоби вибору постачальника для проекту. Мета дипломної роботи: дослідити методи та процеси вибору постачальника для проектів на прикладі компанії що займається перевезеннями, на основі методів fuzzy Decision-Making Trial and Evaluation Laboratory та fuzzy based Analytical Network Process. Результати: розроблено концептуальну модель стратегії вибору постачальника для проектів; удосконалено модель вибору критеріїв оцінки постачальників шляхом інтеграції методів fuzzy DEMATEL та fuzzy based ANP, створено засіб, що реалізує єдину комбіновану модель без допомоги стороннього програмного забезпечення, розроблено метод вибору постачальника проектів, проведено відповідні розрахунки. Програмний продукт було розроблено з використанням мови програмування C#, інтерфейс — використовуючи технології Windows Form.
Thesis: 90 p., 17 tabl., 32 fig., 1 append., 23 sources. Object of research: the process of selecting a supplier for the needs of the project. Subject of research: methods and means of selecting a supplier for a project. The purpose of the thesis: to explore the methods and processes of selecting a vendor for projects for the transportation company based on the methods of the Fuzzy Decision-Making Trial and Evaluation Laboratory and the fuzzy based Analytical Network Process. Results: the conceptual model of the supplier selection strategy for the projects was developed; the model of selection of supplier evaluation criteria has been improved by integrating the methods of fuzzy DEMATEL and fuzzy based ANP, created a tool that implements a single combined model without the help of third-party software, the method of selecting a supplier of projects has been developed, appropriate calculations have been made The software product was developed using the C # programming language, the interface — using the Windows Form technology.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
A abordagem Quality Function Deployment (QFD) integrada a métodos multicritério de apoio à decisão vem sendo amplamente aplicada a projetos de novos produtos, particularmente quando integrada à lógica fuzzy. O objetivo da dissertação é propor um modelo conceitual baseado na abordagem fuzzy ANP-QFD para definir e priorizar requisitos de projeto de novos produtos de defesa à luz de requisitos dos clientes. A pesquisa pode ser considerada descritiva, metodológica e aplicada. A partir dos resultados da revisão bibliográfica e documental sobre os temas centrais da pesquisa, desenvolveu-se um modelo conceitual para definição e priorização de requisitos técnicos de novos produtos de defesa, buscando-se preencher lacunas identificadas na literatura especializada no período 1987-2017. A aplicabilidade do modelo foi demonstrada mediante um estudo empírico no âmbito do Projeto COBRA 2020, uma iniciativa estratégica do Exército brasileiro. Para este estudo, selecionou-se um dos produtos do referido Projeto – um monóculo de visão térmica. Destacam-se como principais contribuições da pesquisa um modelo para definir e priorizar requisitos de projeto de novos produtos de defesa, que considera a complexidade, subjetividade e incerteza como características inerentes a projetos de novos produtos de defesa. Os resultados desta pesquisa poderão ser replicados em outros projetos de novos produtos de defesa – no Centro Tecnológico do Exército – CTEx – e em outras instituições militares envolvidas com atividades de pesquisa, desenvolvimento e inovação (PDeI) no Brasil e no exterior.
The Quality Function Deployment (QFD) approach integrated with multicriteria decision-support methods has been widely applied to development of new product, particularly with the support of fuzzy logic. The objective of this dissertation is to propose a conceptual model based on the fuzzy ANP-QFD approach to define and prioritize project requirements of new defense products. The research can be considered descriptive, applied, and methodological. Based on the results of the bibliographic and documentary review on the central themes of the research, a conceptual model was developed to determine and prioritize project requirements of new defense products, seeking to fill gaps identified during the literature review covering the period of 1987-2017. The applicability of the model was demonstrated by an empirical case study having as experimental context the Project COBRA 2020, a strategic initiative of the Brazilian Army. For this study, one of new products to be developed within this Project was selected – a monocle of thermal vision. The main contribution of the research is a model for determining and prioritizing project requirements of new defense products, which considers the complexity, subjectivity, and uncertainty as inherent characteristics to the design of new defense products. The research findings could be replicated in other projects of new defense products - at the Army Technological Center - CTEx - and other military institutions dealing with research, development and innovation (RDandI) activities in Brazil and abroad.

Zugl.: Saarbrücken, Universiẗat, Diss., 2003 u.d.T.: Hüsselmann, Claus: Unscharfe Informationen in der Unternehmensmodellierung - formale Integration von Unbestimmtheitsaspekten in die ereignisgesteuerte Geschäftsprozessmodellierung.

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In the literature there are several proposals of fuzzi cation of lattices and ideals concepts. Chon in (Korean J. Math 17 (2009), No. 4, 361-374), using the notion of fuzzy order relation de ned by Zadeh, introduced a new notion of fuzzy lattice and studied the level sets of fuzzy lattices, but did not de ne a notion of fuzzy ideals for this type of fuzzy lattice. In this thesis, using the fuzzy lattices de ned by Chon, we de ne fuzzy homomorphism between fuzzy lattices, the operations of product, collapsed sum, lifting, opposite, interval and intuitionistic on bounded fuzzy lattices. They are conceived as extensions of their analogous operations on the classical theory by using this de nition of fuzzy lattices and introduce new results from these operators. In addition, we de ne ideals and lters of fuzzy lattices and concepts in the same way as in their characterization in terms of level and support sets. One of the results found here is the connection among ideals, supports and level sets. The reader will also nd the de nition of some kinds of ideals and lters as well as some results with respect to the intersection among their families. Moreover, we introduce a new notion of fuzzy ideals and fuzzy lters for fuzzy lattices de ned by Chon. We de ne types of fuzzy ideals and fuzzy lters that generalize usual types of ideals and lters of lattices, such as principal ideals, proper ideals, prime ideals and maximal ideals. The main idea is verifying that analogous properties in the classical theory on lattices are maintained in this new theory of fuzzy ideals. We also de ne, a fuzzy homomorphism h from fuzzy lattices L and M and prove some results involving fuzzy homomorphism and fuzzy ideals as if h is a fuzzy monomorphism and the fuzzy image of a fuzzy set ~h(I) is a fuzzy ideal, then I is a fuzzy ideal. Similarly, we prove for proper, prime and maximal fuzzy ideals. Finally, we prove that h is a fuzzy homomorphism from fuzzy lattices L into M if the inverse image of all principal fuzzy ideals of M is a fuzzy ideal of L. Lastly, we introduce the notion of -ideals and - lters of fuzzy lattices and characterize it by using its support and its level set. Moreover, we prove some similar properties in the classical theory of - ideals and - lters, such as, the class of -ideals and - lters are closed under intersection. We also de ne fuzzy -ideals of fuzzy lattices, some properties analogous to the classical theory are also proved and characterize a fuzzy -ideal on operation of product between bounded fuzzy lattices L and M and prove some results.

This research extends fuzzy methods to consider the fuzzy validation of fuzzy land cover data at the sub-pixel level. The study analyses the relationships between fuzzy memberships generated by field survey and those generated from the classification of remotely sensed data. In so doing it examines the variations in the relationship between observed and predicted fuzzy land cover classes. This research applies three land cover classification techniques: Fuzzy sets, Fuzzy c-means and Boolean classification, and develops three models to determine fuzzy land cover change. The first model is dependent on fuzzy object change. The second model depends on the sub-pixel change through a fuzzy change matrix, for both fuzzy sets and fuzzy c-means, to compute the fuzzy change, fuzzy loss and fuzzy gain. The third model is a Boolean change model which evaluates change on a pixel-by-pixel basis. The results show that using a fuzzy change analysis presents a subtle way of mapping a heterogeneous area with common mixed pixels. Furthermore, the results show that the fuzzy change matrix gives more detail and information about land cover change and is more appropriate than fuzzy object change because it deals with sub-pixel change. Finally the research has found that a fuzzy error matrix is more suitable than an error matrix for soft classification validation because it can compare the membership from the field with the classified image. From this research there arise some important points: • Fuzzy methodologies have the ability to define the uncertainties associated with describing the phenomenon itself and the ability to take into consideration the effect of mixed pixels. • This research compared fuzzy sets and fuzzy c-means, and found the fuzzy set is more suit-able than fuzzy c-means, because the latter suffers from some disadvantages, chiefly that the sum of membership values of a data point in all the clusters must be one, so the algorithm has difficulty in handling outlying points. • This research validates fuzzy classifications by determining the fuzzy memberships in the field and comparing them with the memberships derived from the classified image.

Neka je A neprazan skup  i ℒ = (L, ≤) proizvoljna mreža sa nulom i jedinicom. Svako preslikavanje µ: A → L zovemo rasplinuti podskup od A. U ovoj tezi proučavali smo rasplinute posete i relacije rasplinutog poretka. Uveli smo neke nove pojmove: rasplinuta uređena grupa, rasplinuti pozitivan konus, rasplinuti negativan konus, rasplinuta mrežno uređena grupa. Posmatrajući strukturu svih relacija slabog rasplinutog poretka koje su podskup klasične relacije poretka ≤ , došli smo do zaključka da ova struktura predstavlja kompletnu mrežu. Takođe, važan zadatak je bio da ispitamo egzistenciju rasplinute mrežno uređene podgrupe l –uređene grupe koja nije linearno uređena. Bitan rezultat je rasplinuta mrežno uređena podgrupa date mrežno uređene grupe G, koja je konstruisana pomoću mreže svih kompleksnih l –podgrupa od G.
Let A be a nonempty set, and let ℒ = (L, ≤) be a lattice with 0 and 1. The mapping: µ: A → L is called a fuzzy subset of A. In this work we investigated fuzzy posets and fuzzy ordering relations. We introduced some new notions: fuzzy ordered groups, fuzzy positive cone, fuzzy negative cone, fuzzy lattice ordered group. Considering a structure of all weak fuzzy orderings contained in the crisp order ≤, we concluded that this structure represents a complete lattice. Also, an important task was to investigate the existence of a fuzzy lattice ordered subgroup of an l–ordered group which is not linearly ordered. A main result is a fuzzy lattice ordered subgroup of a given lattice ordered group G, which is constructed by the lattice of all convex l-subgroups of G.

Zugl.: Hamburg, Universiẗat, Diss., 1999 u.d.T.: Keuper, Frank : Konzeptionelle Einsatzmöglichkeiten und Erfolgspotentiale der Fuzzy-Set-Theorie in Systemen zur Produktionsplanung und -steuerung bei Auftragsfertigung.
Literaturverz. S. 471 - 499.

Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.

The fuzzball model of a black hole is an attempt to resolve the many paradoxes and puzzles of black hole physics that have revealed themselves over the last century. These badly behaved solutions of general relativity have given physicists one of the few laboratories to test candidate quantum theories of gravity. Though little is known about exactly what lies beyond the event horizon, and what the ultimate fate of matter that falls in to a black hole is, we know a few intriguing and elegant semi-classical results that have kept physicists occupied. Among these are the known black hole entropy and the Hawking radiation process.

Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods. After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities.

El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido. En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente. En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,
Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879
Palancia

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Esta dissertação apresenta uma nova proposta de sistemas (modelos) neuro-fuzzy que possuem, além do tradicional aprendizado dos parâmetros, comuns às redes neurais e aos sistemas nero-fuzzy, as seguintes características: aprendizado de estrutura, a partir do uso de particionamentos recursisvos; número maior de entradas que o comumente encontrado nos sistemas neuro-fuzzy; e regras com hierarquia. A definição da estrutura é uma necessidade que surge quando da implementação de um determinado modelo. Pode-se citar o caso das redes neurais, em que se deve determinar (ou arbitrar) a priori sua estrutura (número de camadas e quantidade de neurônios por camadas) antes de qualquer teste. Um método automático de aprendizado da estrutura é, portanto, uma característica importante em qualquer modelo. Um sistema que também permita o uso de um número maior de entradas é interessante para se abranger um maior número de aplicações. As regras com hierarquia são um subproduto do método de aprendizado de estrutura desenvolvido nestes novos modelos. O trabalho envolveu três partes principais: um levantamento sobre os sistemas neuro-fuzzy existentes e sobre os métodos mais comuns de ajuste de parâmetros; a definição e implementação de dois modelos neuro-fuzzy hierárquicos; e o estudo de casos. No estudo sobre os sistemas neuro-fuzzy(SNF) fez-se um levantamento na bibliografia da área sobre as características principais desses sistemas, incluindo suas virtudes e deficiências. Este estudo gerou a proposta de uma taxonomia para os SNF, em função das características fuzzy neurais. Em virtude deste estudo constataram-se limitações quanto à capacidade de criação de sua própria estrutura e quanto ao número reduzido de entradas possíveis. No que se refere aos métodos de ajuste dos parâmetros abordou-se os métodos mais comuns utilizados nos SNF, a saber: o método dos mínimos quadrados com sua solução através de métodos numéricos iterativos; e o método gradient descent e seus derivados como o BackPropagation e o RProp(Resilient BackPropagation). A definição dos dois novos modelos neuro-fuzzy foi feita a partir do estudo das características desejáveis e das limitações dos SNF até então desenvolvidos. Observou-se que a base de regras dos SNF juntamente com os seus formatos de particionamento dos espaços de entrada e saída têm grande influência sobre o desempenho e as limitações destes modelos. Assim sendo, decidiu-se utilizar uma nova forma de particionamento que eliminasse ou reduzisse as limitações existentes- os particionamentos recursivos. Optou-se pelo uso dos particionamentos Quadtree e BSP, gerando os dois modelos NFHQ (Neuro-Fuzzy Hierárquico Quadree) e NFHB (Neiro-Fuzzy Hierárquico BSP). Com o uso de particionamentos obteve-se um nova classe de SNF que permitiu além do aprendizado dos parâmetros, também o aprendizado dos parâmetros. Isto representa um grande diferencial em relação aos SNF tradicionais, além do fato de se conseguir extender o limite do número de entradas possíveis para estes sistemas. No estudo de casos, os dois modelos neurofuzzy hierárquicos foram testados 16 casos diferentes, entre as aplicações benchmarks mais tradicionais da área e problemas com maior número de entradas. Entre os casos estudados estão: o conjunto de dados IRIS; o problema das duas espirais; a previsão da série caótica de Mackey- Glass; alguns sistemas de diagnóstico e classificação gerados a partir de conjuntos de dados comumente utilizados em artigos de machine learning e uma aplicação de previsão de carga elétrica. A implementação dos dois novos modelos neuro-fuzzy foi efetuada em linguagem pascal e com o uso de um compilador de 32 bits para micros da linha PC (Pentium) com sistema operacional DOS 32 bits, Windows, ou Linux. Os testes efetuados demostraram que: esses novos modelos se ajustam bem a qualquer conj
This dissertation presents a new proposal of neurofuzzy systems (models), which present, in addition to the learning capacity (which are common to the neural networks and neurofuzzy systems) the following features: learning of the structure; the use of recursive partitioning; a greater number of inputs than usually allowed in neurofuzzy systems; and hierarchical rules. The structure´s definition is needed when implementing a certain model. In the neural network case, for example, one must, first of all, estabilish its structure (number of layers and number of neurons per layers) before any test is performed. So, an important feature for any model is the existence of an automatic learning method for creating its structure. A system that allows a larger number of inputs is also important, in order to extend the range of possible applications. The hierarchical rules feature results from the structure learning method developed for these two models. The work has involved three main parts: study of the existing neurofuzzy systems and of the most commom methods to adjust its parameters; definition and implementation of two hierarchical neurofuzzy models; and case studies. The study of neurofuzzy systems (NFS) was accomplished by creating a survey on this area, including advantages, drawbacks and the main features of NFS. A taxonomy about NFS was then proposed, taking into account the neural and fuzzy features of the existing systems. This study pointed out the limitations of neurofuzzy systems, mainly their poor capability of creating its own structure and the reduced number of allowed inputs. The study of the methods for parameter adjustment has focused on the following algorithms: Least Square estimator (LSE) and its solutions by numerical iterative methods; and the basic gradient descent method and its offsprings such as Backpropagation and Rprop (Resilient Backpropagation). The definition of two new neurofuzzy models was accomplished by considering desirable features and limitations of the existing NFS. It was observed that the partitioning formats and rule basis of the NFS have great influence on its performance and limitations. Thus, the decision to use a new partitioning method to remove or reduce the existing limitations - the recursive partitioning. The Quadtree and BSP partitioning were then adopted, generating the so called Quadree Hierarchical Neurofuzzy model (NFHQ) and the BSP hierarchical Neurofuzzy model (NFHB). By using these kind os partitioning a new class of NFS was obtained allowing the learning of the structure in addition to parameter learning. This Feature represents a great differential in relation to the traditional NFS, besides overcoming the limitation in the number of allowed inputs. In the case studies, the two neurofuzzy models were tested in 16 differents cases, such as traditional benchmarks and problems with a greater number of inputs. Among the cases studied are: the IRIS DATA set; the two spirals problem; the forecasting of Mackey-Glass chaotic time series; some diagnosis and classifications problems, found in papers about machine learning; and a real application involving load forecasting. The implementation of the two new neurofuzzy models was carried out using a 32 bit Pascal compiler for PC microcomputers using DOS or Linux operating system. The tests have shown that: these new models are able to adjust well any data sets; they create its own struture; they adjust its parameters, presenting a good generalization performance; and automatically extract the fuzzy rules. Beyond that, applications with a greater number of inputs for these neurofuzzy models. In short two neurofuzzy models were developed with the capability of structure learning, in addition to parameter learning. Moreover, these new models have good interpretability through hierarchical fuzzy rules. They are not black coxes as the neural networks.

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Este trabalho apresenta um modelo de Regressão Linear Nebulosa por Partes(RLNP). Trata-se de uma estrutura que envolve modelos de regressão linear por partes ponderadas por pertinências advindas da lógica nebulosa. Este modelo é comparado com o modelo de regressão linear. Os resultados mostram que o RLNP consegue identificar a estrutura não-linear dos dados simulados e que na maioria dos casos ele possui bom poder de ajuste.
In this dissertation a Fuzzy Piece-Wise Linear Regressive model FPLieR is developed. The model´s structure combines linear regressive models with fuzzy logic´s grade of membership in a piece-wise fashion. A comparision is made between this model and the linear regression one. The results show that FPLieR is able to find the linear substructure of simulated data and that in most cases it presents a good fit.

The broad goal in this thesis is to enumerate elements and fuzzy subsets of a finite set enjoying some useful properties through the well-known counting technique of the principle of inclusion-exclusion. We consider the set of membership values to be finite and uniformly spaced in the real unit interval. Further we define an equivalence relation with regards to the cardinalities of fuzzy subsets providing the Möbius function and Möbius inversion in that context.

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Esta dissertação investiga a utilização de sistemas Neuro- Fuzzy Hierárquicos BSP (Binary Space Partitioning) para classificação de padrões e para extração de regras fuzzy em bases de dados. O objetivo do trabalho foi criar modelos específicos para classificação de registros a partir do modelo Neuro-Fuzzy Hierárquico BSP que é capaz de gerar sua própria estrutura automaticamente e extrair regras fuzzy, lingüisticamente interpretáveis, que explicam a estrutura dos dados. O princípio da tarefa de classificação de padrões é descobrir relacionamentos entre os dados com a intenção de prever a classe de um padrão desconhecido. O trabalho consistiu fundamentalmente de quatro partes: um estudo sobre os principais métodos de classificação de padrões; análise do sistema Neuro-Fuzzy Hierárquico BSP (NFHB) original na tarefa de classificação; definição e implementação de dois sistemas NFHB específicos para classificação de padrões; e o estudo de casos. No estudo sobre os métodos de classificação foi feito um levantamento bibliográfico da área, resultando em um "survey" onde foram apresentadas as principais técnicas utilizadas para esta tarefa. Entre as principais técnicas destacaram-se: os métodos estatísticos, algoritmos genéticos, árvores de decisão fuzzy, redes neurais, e os sistemas neuro-fuzzy. Na análise do sistema NFHB na classificação de dados levou- se em consideração as peculiaridades do modelo, que possui: aprendizado da estrutura, particionamento recursivo do espaço de entrada, aceita maior número de entradas que os outros sistemas neuro-fuzzy, além de regras fuzzy recursivas. O sistema NFHB, entretanto, não é um modelo exatamente desenvolvido para classificação de padrões. O modelo NFHB original possui apenas uma saída e para utilizá- lo como um classificador é necessário criar um critério de faixa de valores (janelas) para representar as classes. Assim sendo, decidiu-se criar novos modelos que suprissem essa deficiência. Foram definidos dois novos sistemas NFHB para classificação de padrões: NFHB-Invertido e NFHB-Class. O primeiro utiliza a arquitetura do modelo NFHB original no aprendizado e em seguida a inversão da mesma para a validação dos resultados. A inversão do sistema consistiu de um meio de adaptar o novo sistema à tarefa específica de classificação, pois passou-se a ter o número de saídas do sistema igual ao número de classes ao invés do critério de faixa de valores utilizado no modelo NFHB original. Já o sistema NFHB-Class utilizou, tanto para a fase de aprendizado, quanto para a fase de validação, o modelo NFHB original invertido. Ambos os sistemas criados possuem o número de saídas igual ao número de classes dos padrões, o que representou um grande diferencial em relação ao modelo NFHB original. Além do objetivo de classificação de padrões, o sistema NFHB-Class foi capaz de extrair conhecimento em forma de regras fuzzy interpretáveis. Essas regras são expressas da seguinte maneira: SE x é A e y é B então padrão pertence à classe Z. Realizou-se um amplo estudo de casos, abrangendo diversas bases de dados Benchmark para a tarefa de classificação, tais como: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders e Heart Disease, e foram feitas comparações com diversos modelos e algoritmos de classificação de padrões. Os resultados encontrados com os modelos NFHB-Invertido e NFHB-Class mostraram-se, na maioria dos casos, superiores ou iguais aos melhores resultados encontrados pelos outros modelos e algoritmos aos quais foram comparados.O desempenho dos modelos NFHB-Invertido e NFHB-Class em relação ao tempo de processamento também se mostrou muito bom. Para todas as bases de dados descritas no estudo de casos (capítulo 8), os modelos convergiram para uma ótima solução de classificação, além da extração das regras fuzzy, em
This dissertation investigates the use of Neuro-Fuzzy Hierarchical BSP (Binary Space Partitioning) systems for pattern classification and extraction of fuzzy rules in databases. The objective of this work was to create specific models for the classification of registers based on the Neuro-Fuzzy BSP model that is able to create its structure automatically and to extract linguistic rules that explain the data structure. The task of pattern classification is to find relationships between data with the intention of forecasting the class of an unknown pattern. The work consisted of four parts: study about the main methods of the pattern classification; evaluation of the original Neuro-Fuzzy Hierarchical BSP system (NFHB) in pattern classification; definition and implementation of two NFHB systems dedicated to pattern classification; and case studies. The study about classification methods resulted in a survey on the area, where the main techniques used for pattern classification are described. The main techniques are: statistic methods, genetic algorithms, decision trees, neural networks, and neuro-fuzzy systems. The evaluation of the NFHB system in pattern classification took in to consideration the particularities of the model which has: ability to create its own structure; recursive space partitioning; ability to deal with more inputs than other neuro-fuzzy system; and recursive fuzzy rules. The original NFHB system, however, is unsuited for pattern classification. The original NFHB model has only one output and its use in classification problems makes it necessary to create a criterion of band value (windows) in order to represent the classes. Therefore, it was decided to create new models that could overcome this deficiency. Two new NFHB systems were developed for pattern classification: NFHB-Invertido and NFHB-Class. The first one creates its structure using the same learning algorithm of the original NFHB system. After the structure has been created, it is inverted (see chapter 5) for the generalization process. The inversion of the structure provides the system with the number of outputs equal to the number of classes in the database. The second system, the NFHB-Class uses an inverted version of the original basic NFHB cell in both phases, learning and validation. Both systems proposed have the number of outputs equal to the number of the pattern classes, what means a great differential in relation to the original NFHB model. Besides the pattern classification objective, the NFHB- Class system was able to extract knowledge in form of interpretable fuzzy rules. These rules are expressed by this way: If x is A and y is B then the pattern belongs to Z class. The two models developed have been tested in many case studies, including Benchmark databases for classification task, such as: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders and Heart Disease, where comparison has been made with several traditional models and algorithms of pattern classification. The results found with NFHB-Invertido and NFHB-Class models, in all cases, showed to be superior or equal to the best results found by the others models and algorithms for pattern classification. The performance of the NFHB- Invertido and NFHB-Class models in terms of time-processing were also very good. For all databases described in the case studies (chapter 8), the models converged to an optimal classification solution, besides the fuzzy rules extraction, in a time-processing inferior to a minute.
Esta disertación investiga el uso de sistemas Neuro- Fuzzy Herárquicos BSP (Binary Space Partitioning) en problemas de clasificación de padrones y de extracción de reglas fuzzy en bases de datos. El objetivo de este trabajo fue crear modelos específicos para clasificación de registros a partir del modelo Neuro-Fuzzy Jerárquico BSP que es capaz de generar automáticamente su propia extructura y extraer reglas fuzzy, lingüisticamente interpretables, que explican la extructura de los datos. El principio de la clasificación de padrones es descubrir relaciones entre los datos con la intención de prever la clase de un padrón desconocido. El trabajo está constituido por cuatro partes: un estudio sobre los principales métodos de clasificación de padrones; análisis del sistema Neuro-Fuzzy Jerárquico BSP (NFHB) original en la clasificación; definición e implementación de dos sistemas NFHB específicos para clasificación de padrones; y el estudio de casos. En el estudio de los métodos de clasificación se realizó un levatamiento bibliográfico, creando un "survey" donde se presentan las principales técnicas utilizadas. Entre las principales técnicas se destacan: los métodos estadísticos, algoritmos genéticos, árboles de decisión fuzzy, redes neurales, y los sistemas neuro-fuzzy. En el análisis del sistema NFHB para clasificación de datos se tuvieron en cuenta las peculiaridades del modelo, que posee : aprendizaje de la extructura, particionamiento recursivo del espacio de entrada, acepta mayor número de entradas que los otros sistemas neuro-fuzzy, además de reglas fuzzy recursivas. El sistema NFHB, sin embargo, no es un modelo exactamente desarrollado para clasificación de padrones. El modelo NFHB original posee apenas una salida y para utilizarlo conmo un clasificador fue necesario crear un criterio de intervalos de valores (ventanas) para representar las clases. Así, se decidió crear nuevos modelos que supriman esta deficiencia. Se definieron dos nuevos sistemas NFHB para clasificación de padrones: NFHB- Invertido y NFHB-Clas. El primero utiliza la arquitectura del modelo NFHB original en el aprendizaje y en seguida la inversión de la arquitectura para la validación de los resultados. La inversión del sistema es un medio para adaptar el nuevo sistema, específicamente a la clasificación, ya que el sistema pasó a tener número de salidas igual al número de clases, al contrario del criterio de intervalo de valores utilizado en el modelo NFHB original. En el sistema NFHB-Clas se utilizó, tanto para la fase de aprendizajeo, cuanto para la fase de validación, el modelo NFHB original invertido. Ambos sistemas poseen el número de salidas igual al número de clases de los padrones, lo que representa una gran diferencia en relación al modelo NFHB original. Además del objetivo de clasificación de padrones, el sistema NFHB-Clas fue capaz de extraer conocimento en forma de reglas fuzzy interpretables. Esas reglas se expresan de la siguiente manera: Si x es A e y es B entonces el padrón pertenece a la clase Z. Se realizó un amplio estudio de casos, utilizando diversas bases de datos Benchmark para la clasificación, tales como: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders y Heart Disease. Los resultados se compararon con diversos modelos y algoritmos de clasificación de padrones. Los resultados encontrados con los modelos NFHB-Invertido y NFHB-Clas se mostraron, en la mayoría de los casos, superiores o iguales a los mejores resultados encontrados por los otros modelos y algoritmos con los cuales fueron comparados. El desempeño de los modelos NFHB-Invertido y NFHB-Clas en relación al tiempo de procesamiento tambiém se mostró muy bien. Para todas las bases de datos descritas en el estudio de casos (capítulo 8), los modelos convergieron para una solución óptima, además de la extracción de las reglas fuzzy, con tiemp

Many classical control methods are based upon assumptions of linearity and stationarity of the process to be controlled. For the case of motion control of a land vehicle in an unstructured outdoor environment these assumptions do not hold, due to complex vehicle interactions with its surroundings and time--varying environmental conditions. The large number of possible future platforms leads to the desire to produce motion controllers which are generally applicable to a wide range of vehicles with little a priori knowledge of vehicle dynamics. Intelligent, self--learning, systems promise many of the desired features for such controllers. This thesis investigates the use of intelligent controllers for autonomous land vehicle motion control. A new class of fuzzy controller, the indirect adaptive fuzzy controller is proposed as a possible solution to this problem. This controller is then developed by combining on--line adaptive modelling with model causality inversion and on--line controller design. The resulting controller is an analogue of the indirect adaptive algebraic controller. A major advantages of this method is the separation of model convergence and control loops enabling the two aspects to be analysed separately. Demonstration of this work has been achieved by a series of simulation tests using a variety of vehicle models. A conventional front wheel steer road vehicle model has been used as well as two IFAC benchmark control problems (ship autopilot and passenger bus) to investigate the properties of the controller. To test the controller with realistic demand signals, a static rule-based piloting system has also been developed. These simulations have demonstrated i) the successful control of systems with little a priori vehicle knowledge ii) ability to adapt to continuous and sudden parametric changes in the process iii) good noise rejection properties iv) good disturbance rejection properties and v) ability to adapt to stationary loop non--linearities.

A seleção de fornecedores tem impacto significante no custo e na qualidade de produtos manufaturados. Por isso, a seleção de fornecedores passou a ser vista como uma atividade bastante crítica para o desempenho da empresa compradora. Muitos estudos da literatura propõem o uso dos métodos multicritério fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) e fuzzy AHP (Analytic Hierarchy Process) para apoiar a seleção de fornecedores. Contudo, não são encontrados estudos que avaliem o desempenho destes métodos quando usados neste domínio de problema. Diante desta lacuna, este estudo compara os métodos fuzzy TOPSIS (CHEN, 2000) e fuzzy AHP (CHANG, 1996) no apoio à seleção de fornecedores. Esta pesquisa utiliza uma abordagem quantitativa descritiva empírica, baseada em modelagem e simulação. Os métodos fuzzy TOPSIS e fuzzy AHP foram aplicados em um caso ilustrativo de seleção de fornecedores. O desempenho dos fornecedores e o peso dos critérios foram avaliados por um especialista de uma empresa. Modelos de simulação foram implementados usando MATLAB® e aplicados na seleção de fornecedores de uma empresa de uma cadeia de suprimentos automotiva. Cinco fornecedores foram avaliados em relação à qualidade, custo, entrega, perfil e relacionamento. O peso dos critérios e o desempenho dos fornecedores foi avaliado por meio da opinião de um especialista da empresa. Posteriormente, os métodos fuzzy TOPSIS e fuzzy AHP foram comparados em relação à capacidade de apoiar a decisão em grupo, qualificação de fornecedores, escolha final de fornecedores, situações de compra e modelagem de decisões sob incerteza. A eficiência dos métodos em relação à complexidade computacional e à interação requerida com o usuário também foi comparada. Os resultados mostraram que o fuzzy TOPSIS é mais flexível e mais adequado que o fuzzy AHP para modelar diferentes tipos de cenários de seleção de fornecedores. A realização desta discussão é sugerida por Ertugrul e Karakasoglu (2008), e é relevante para ajudar pesquisadores e gestores na escolha de abordagens efetivas para lidar com diferentes cenários de seleção de fornecedores.
Supplier selection has a significant influence on the cost, quality and delivery of products of the buying company. Therefore, supplier selection has become a very critical activity to the performance of the buying company. Several studies presented in the literature propose the use of fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and fuzzy AHP (Analytic Hierarchy Process) to aid the decision process of supplier selection. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of the methods fuzzy TOPSIS (Chen, 2000) and fuzzy AHP (Chang, 1996) applied to the problem of supplier selection. A descriptive quantitative approach was adopted as the research method. Algorithms of the methods fuzzy TOPSIS and fuzzy AHP were developed in Matlab© and applied to the selection of suppliers of a company in the automotive production chain. Five suppliers were evaluated regarding quality of conformance, cost, delivery, profile and relationship. The weight of the criteria and the performance of the suppliers were evaluated by specialist opinion from the studied company. The methods Fuzzy TOPSIS e Fuzzy AHP were compared in terms of ability to support the group decision, supplier qualification, final choice of suppliers, buying situations and modeling decisions under uncertainty. The efficiency of the methods with respect to computational complexity and the required user interaction was also compared. The comparative analysis shows that Fuzzy TOPSIS presents better than Fuzzy AHP performance, especially in scenarios in wich many alternatives are evaluated. Thus, Fuzzy TOPSIS is more flexible and appropriate than Fuzzy AHP to deal with supplier selection problem. This paper presents a new study, comparing the methods Fuzzy TOPSIS and Fuzzy AHP. As commented by Ertugrul and Karakasoglu (2008), a study such as this can contribute to the advance of knowledge, helping researchers and practitioners choosing more effective approaches to supplier selection.

The fuzzball model of a black hole is an attempt to resolve the many paradoxes and puzzles of black hole physics that have revealed themselves over the last century. These badly behaved solutions of general relativity have given physicists one of the few laboratories to test candidate quantum theories of gravity. Though little is known about exactly what lies beyond the event horizon, and what the ultimate fate of matter that falls in to a black hole is, we know a few intriguing and elegant semi-classical results that have kept physicists occupied. Among these are the known black hole entropy and the Hawking radiation process.

We start by discussing fuzzy sets and the algebra of fuzzy sets. We consider some properties of fuzzy modeling tools. This is followed by considering the Mamdani and Sugeno models for designing fuzzy controllers. Various methods for using sets of data for desining controllers are discussed. This is followed by a chapter illustrating the use of genetic algorithms in designing and optimizing fuzzy controllers.Finally we look at some previous applications of fuzzy control in telecommunication networks, and illustrate a simple application that was developed as part of the present work.

Cluster analysis, the automatic procedure by which large data sets can be split into similar groups of objects (clusters), has innumerable applications in a wide range of problem domains. Improvements in clustering quality (as captured by internal validation indexes) and speed (number of iterations until cost function convergence), the main focus of this work, have many desirable consequences. They can result, for example, in faster and more precise detection of illness onset based on symptoms or it could provide investors with a rapid detection and visualization of patterns in financial time series and so on. Partitional clustering, one of the most popular ways of doing cluster analysis, can be classified into two main categories: hard (where the clusters discovered are disjoint) and soft (also known as fuzzy; clusters are non-disjoint, or overlapping). In this work we consider how improvements in the speed and solution quality of the soft partitional clustering algorithm Fuzzy C-means (FCM) can be achieved through more careful and informed initialization based on data content. By carefully selecting the cluster centers in a way which disperses the initial cluster centers through the data space, the resulting FCM++ approach samples starting cluster centers during the initialization phase. The cluster centers are well spread in the input space, resulting in both faster convergence times and higher quality solutions. Moreover, we allow the user to specify a parameter indicating how far and apart the cluster centers should be picked in the dataspace right at the beginning of the clustering procedure. We show FCM++'s superior behaviour in both convergence times and quality compared with existing methods, on a wide rangeof artificially generated and real data sets. We consider a case study where we propose a methodology based on FCM++for pattern discovery on synthetic and real world time series data. We discuss a method to utilize both Pearson correlation and Multi-Dimensional Scaling in order to reduce data dimensionality, remove noise and make the dataset easier to interpret and analyse. We show that by using FCM++ we can make an positive impact on the quality (with the Xie Beni index being lower in nine out of ten cases for FCM++) and speed (with on average 6.3 iterations compared with 22.6 iterations) when trying to cluster these lower dimensional, noise reduced, representations of the time series. This methodology provides a clearer picture of the cluster analysis results and helps in detecting similarly behaving time series which could otherwise come from any domain. Further, we investigate the use of Spherical Fuzzy C-Means (SFCM) with the seeding mechanism used for FCM++ on news text data retrieved from a popular British newspaper. The methodology allows us to visualize and group hundreds of news articles based on the topics discussed within. The positive impact made by SFCM++ translates into a faster process (with on average 12.2 iterations compared with the 16.8 needed by the standard SFCM) and a higher quality solution (with the Xie Beni being lower for SFCM++ in seven out of every ten runs).

Fuzzy control is a rule based type of control that aims to imitate the human's ability to express a control policy using linguistic rules, and to reason using those rules to control a system. Fuzzy control is nonlinear and not dependent on a precise mathematical description of the plant, and is therefore more easily applied to systems such as industrial processes that are hard to model. An overview is given of the fuzzy controller, along with descriptions of applications and theoretical approaches to designing and analyzing the controller.

The selfâ organizing controller is able to generate or modify its rules in real time based on the system performance. It was tested to determine how well it was able to learn a quality control policy. The selfâ organizing controller was found to exhibit poor steady state performance, and to be equally likely to learn poor control as to learn good control. It was not found to eliminate the need for careful tuning of the controller parameters and gains.

Master of Science

With the emergence of XML as a standard for data representation, particularly on the web, the need for intelligent query languages that can operate on XML documents with structural heterogeneity has recently gained a lot of popularity. Traditional Information Retrieval and Database approaches have limitations when dealing with such scenarios. Therefore, fuzzy (flexible) approaches have become the predominant. In this thesis, we propose a new approach for approximate XML query matching and rewriting which aims at achieving soft matching of XML queries with XML data sources following different schemas. Unlike traditional querying approaches, which require exact matching, the proposed approach makes use of Intuitionistic Fuzzy Trees to achieve approximate (soft) query matching. Through this new approach, not only the exact answer of a query, but also approximate answers are retrieved. Furthermore, partial results can be obtained from multiple data sources and merged together to produce a single answer to a query. The proposed approach introduced a new tree similarity measure that considers the minimum and maximum degrees of similarity/inclusion of trees that are based on arc matching. New techniques for soft node and arc matching were presented for matching queries against data sources with highly varied structures. A prototype was developed to test the proposed ideas and it proved the ability to achieve approximate matching for pattern queries with a number of XML schemas and rewrite the original query so that it obtain results from the underlying data sources. This has been achieved through several novel algorithms which were tested and proved efficiency and low CPU/Memory cost even for big number of data sources.

We present data structures and algorithms for sets and some generalizations thereof (fuzzy sets, multisets, and fuzzy multisets) available for R through the sets package. Fuzzy (multi-)sets are based on dynamically bound fuzzy logic families. Further extensions include user-definable iterators and matching functions. (author´s abstract)
Series: Research Report Series / Department of Statistics and Mathematics

碩士
國立臺灣科技大學
工業管理系
101
Studies have proved that enterprises addressing green supply chain management (GSCM) problem may get apparent improvement to their stockholder interests. According to the report of Carbon Disclosure Project (CDP) in 2012, more than 80% of the carbon emissions are generated from the suppliers’ activities in a company’s operation. Many members of the project claimed that they will reassess their suppliers as soon as possible. Therefore, to combine the carbon management issue and supplier evaluating problem turns to be a very crucial task. This study intends to develop a framework of the supplier evaluating process in carbon management by integrating fuzzy ANP and fuzzy TOPSIS approaches. Thirteen criteria of carbon management under four dimensions were identified by literature review and modified according to the opinion of seven experts in environmental department. The model result on a case illustration shows that three of the most important criteria are “Carbon governance,” “Carbon policy” and “Carbon reduction targets.” The proposed hybrid methodology is believed to have great ability explaining the vagueness of decision maker’s expression and has better power of discrimination to evaluating suppliers in carbon management.

碩士
國立勤益科技大學
工業工程與管理系
97
Enhancing the competitiveness of a company is essential for an entrprise to survive in the fiercely competitive global environment. The acquisition of advanced technology is one of the usable strategies for a company. However, an ineffective acquisition approach not only jeopardizes the competitiveness of a company but also causes delay for products to enter the market. As a result, the primary issue for an entrprise to consider is how to make the right technology acquisition at the right time to satisfy the need of an entrprise. We propose a mechanism for selecting technology acquisition mode. It is a mechanism for decision-maker to implement a practical decision making. In addition, the success of a strategic decision-making depends on both its proximity to the environment and level of its complexity of the decision-making process. Fuzzy analytical networks process not only handles the dynamic interdependence situation of a decision-making problem but also addresses the vagueness when the decision is made. The supermatrix is applied to handle the dependent situation between the influence level and criteria level. However, the influence level belongs to external factor. The external factor gives the decision-maker a chance to review if the intensity of the motive obtained for the mechanism is consistent with that in his mind. If it is so, it can used to pairwise comparison; on the other hand, it should reconsider the weighting process, too. In this research, the fuzzy ANP is applied to construct a general mechanism for discussion of the technology acquisition problem. Besides, a case study of a tool manufacturing company is employed to illustrate the effectiveness of the proposed mechanism.

碩士
中華大學
工業管理學系碩士班
99
To confront the global warming problem and the increase in environmental consciousness, many countries have devised various environmental protection policies. For instance, with the Energy-using Product Directive (2005/32/EC), the European Commission has been addressing energy-using and energy-related products which have a considerable impact on the energy consumption in the market. Therefore, many international companies and original design manufacturing (ODM) manufacturers have aimed to promote green products actively, while the communities are paying attention to the environmental protection of the enterprises. In addition, the international environmental issues have built up some technical non-tariff barriers to trade. Therefore, the traditional supplier selection model is no longer compatible with the environmental requirements. The purpose of this study thus aims to incorporate the concept of carbon reduction and environmental considerations in designing a supplier selection model. The relationships among criteria are determined first by fuzzy decision making trial and evaluation laboratory (Fuzzy DEMATEL), and fuzzy analytic network process (FANP) model is constructed next to determine the weights of performance criteria and to obtain the overall performance of suppliers. The model can generate a list of criteria which are the most important for firms to assess the performance of suppliers and to give directions for suppliers to improve their performances.

碩士
中華大學
科技管理學系碩士班
100
The issue of reducing green house gas (GHG) emission was emphasized in the Kyoto Protocol in 1997 and the United Nations Climate Change Conference (COP15) in Copenhagen in 2009. The major cause of green house gas is people’s extensive usage of fossil fuel. However, to sustain human cultural development, we need to use less fossil fuel, and consequently, the development of renewable energy becomes an important task. Renewable energy includes wind energy, solar energy, biomass energy, ocean power energy, thermal energy, geothermal energy, hydropower energy, and so on. Solar energy has been developed widely and is one of the most popular energy sources for applications. However, currently solar products suffer a large difficulty in high production cost with low PV conversion efficiency. Hence, how to develop new solar product is an important issue. This research aims to construct an integrated NPD model for a solar module company to understand the critcal factors in the new product development. A comprehensive literature review is performed first to understand the customer needs of solar modules from solar assembly companies. Because fuzziness and uncertainty often exist in experts’ evaluation process, this research adopts Fuzzy Delphi Method (FDM) to select the most critical factors. Next, Fuzzy DEMATEL is applied to clarify the interrelationship among factors. Finally, Fuzzy Analytic Network Process (FANP) and Qualify Function Deployment (QFD) are used to calculate the weights of the engineering characteristics. The results can be references for solar cell NPD in the industry in the future.

碩士
國立高雄應用科技大學
工業工程與管理系碩士班
94
Owing to the impact of the knowledge economy industry globalization nowadays, enterprises must depend on continuous exploration of knowledge, sharing, application and innovation to strengthen its competition advantages. Then, the goal of improving management performance may be achieved. But in facing of the ongoing challenges and impacts from knowledge economies, the planning and constructing of knowledge management stratagems show more important to enterprise. Therefore, this research combines three methods, the Fuzzy Quality Function Deployment (FQFD), Analytic Network Process (ANP), and Zero-One Goal Programming (ZOGP), to investigate and understand the attribute that knowledge management process value. Finally, an example is presented as a substantial analysis model to explain the application of the methodology provided in this research to planners. Hope that this also offers a comprehensive reference to knowledge management technique criterion factors estimate and choice decision-makers. The technique characteristics that knowledge management technique criterion factors estimate and choice model offers through the integration of FQFD, ANP, and ZOGP are threefold: The technique characteristic chosen in knowledge acquiring process are the ability to acquire the knowledge about the market demand to the company, to define the knowledge of company/department, and to apply the methods of analytic statistics. The technique characteristic chosen in knowledge transferring process are having the ability on complete stratagems and policies of human resource (HR), accommodating HR to the design and development of the organization/system, matching employees’ specialty with his/her profession/career, planning integrated training education, establishing sane employee learning system. The technique characteristic chosen in knowledge applying process is the ability to stretch the knowledge of the company/department.