Made available in DSpace on 2014-06-12T17:36:57Z (GMT). No. of bitstreams: 2 arquivo3684_1.pdf: 1677567 bytes, checksum: f1c5ab807c32df464cd25a1eb8e5dde9 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2008 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / O crescimento do setor de serviço pode ser detectado já na Grécia Clássica, tendo estagnado apenas durante o período da Revolução Industrial, no século XVIII, tornando a crescer na segunda metade do século XX. Todo esse período de prosperidade se reflete nos principais indicadores utilizados mundialmente para determinar a magnitude de cada setor da economia: a taxa de ocupação da mão-de-obra, a participação no Produto Interno Bruto (PIB) de cada país e o valor adicionado. Tais indicadores comprovam que o setor de serviço já é o setor da economia mundial que promove a maior movimentação de capital. Apesar dessa importância econômica, muitas organizações têm relegado a segundo plano investimentos nas suas atividades de serviços. Este trabalho busca apresentar um detalhamento do setor de serviços, com um enfoque na avaliação da qualidade, caracterizando suas particularidades, tipologias e principais modelos de avaliação. Posteriormente, é proposta uma técnica que permita uma abordagem quantitativa quando da avaliação da qualidade. Para tanto, são utilizados conjuntos fuzzy no tratamento dos dados, permitindo uma visão mais flexível e adequada para as características do setor de serviços e, na seqüência, uma extensão da Technique for Order Performance by Similarity to Ideal Solution TOPSIS, que informa os gestores da distância do atual nível de qualidade da empresa, se comparado com uma empresa de qualidade perfeita por meio de uma avaliação global. A mesma técnica foi utilizada para detectar variações no nível de qualidade durante o período pesquisado através de uma avaliação estratificada. Por fim, a proposta é aplicada em um plano de saúde que obteve um resultado satisfatório na avaliação global, porém ficou evidente a possibilidade de melhoria em todos os critérios avaliados, sobretudo na cobertura do plano. Na avaliação estratificada, as variações no nível de qualidade, que ocorreram durante a pesquisa e que dificultam a fidelização do cliente, foram explicitadas

This thesis considers the neural network learning control of a variable-geometry automotive active suspension system which combines most of the benefits of active suspension systems with low energy consumption. Firstly, neural networks are applied to the control of various simplified automotive active suspensions, in order to understand how a neural network controller can be integrated with a physical dynamic system model. In each case considered, the controlled system has a defined objective and the minimisation of a cost function. The neural network is set up in a learning structure, such that it systematically improves the system performance via repeated trials and modifications of parameters. The learning efficiency is demonstrated by the given system performance in agreement with prior results for both linear and non-linear systems. The above simulation results are generated by MATLAB and the Neural Network Toolbox. Secondly, a half-car model, having one axle and an actuator on each side, is developed via the computer language, AUTOSIM. Each actuator varies the ratio of the spring/damper unit length change to wheel displacement in order to control each wheel rate. The neural network controller is joined with the half-car model and learns to reduce the defined cost function containing a weighted sum of the squares of the body height change, body roll and actuator displacements. The performances of the neurocontrolled system are compared with those of passive and proportional-plusdifferential controlled systems under various conditions. These involve various levels of lateral force inputs and vehicle body weight changes. Finally, energy consumption of the variable-geometry system, with either the neurocontrol or proportional-plus-differential control, is analysed using an actuator model via the computer simulation package, SIMULINK. The simulation results are compared with those of other actively-controlled suspension systems taken from the literature.

Thesis (M. Phil.)--Chinese University of Hong Kong, 1998. / Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.

Cellular automata (CA) are discrete dynamical systems comprised of a lattice of finite-state cells. At each time step, each cell updates its state as a function of the previous state of itself and its neighbours. Fuzzy cellular automata (FCA) are a real-valued extension of Boolean cellular automata which "fuzzifies" Boolean logic in the transition function using real values between zero and one (inclusive). To date, FCA have only been studied in disjunctive normal form (DNF). In this thesis, we study FCA in conjunctive normal form (CNF). We classify FCA in CNF both analytically and empirically. We compare these classes to their DNF counterparts. We prove that certain FCA exhibit chaos in CNF, in contrast to the periodic behaviours of DNF FCA. We also briefly explore five different forms of fuzzy logic, and suggest further study. In support of this research, we introduce novel methods of simulating and visualizing FCA.

In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system. In order to make the type-2 fuzzy logic system reasonable and reliable, a new simple and novel statistical method to decide interval-valued fuzzy membership functions and a new probability type reduced reasoning method for the interval-valued fuzzy logic system are proposed in this thesis. In order to optimize this particle system’s performance, we adopt genetic algorithm (GA) to adjust parameters. The applications for the new system are performed and results have shown that the developed method is more accurate and robust to design a reliable fuzzy logic system than type-1 method and the computation of our proposed method is more efficient.

Cellular automata (CA) are discrete dynamical systems comprised of a lattice of finite-state cells. At each time step, each cell updates its state as a function of the previous state of itself and its neighbours. Fuzzy cellular automata (FCA) are a real-valued extension of Boolean cellular automata which "fuzzifies" Boolean logic in the transition function using real values between zero and one (inclusive). To date, FCA have only been studied in disjunctive normal form (DNF). In this thesis, we study FCA in conjunctive normal form (CNF). We classify FCA in CNF both analytically and empirically. We compare these classes to their DNF counterparts. We prove that certain FCA exhibit chaos in CNF, in contrast to the periodic behaviours of DNF FCA. We also briefly explore five different forms of fuzzy logic, and suggest further study. In support of this research, we introduce novel methods of simulating and visualizing FCA.

This thesis examines the policy problem of economic unit definition from the perspective of the regional economist. The regional economist faces the challenge of disaggregating macroeconomic activity into subparts that accurately reflect the actual economic organization of a country or region. Such an exercise is important because the Governments of many developed countries rely on it to allocate scarce public resources. In the United States, the Bureau of Economic Analysis of the U.S. Department of Commerce is responsible for regional economic unit definition. To meet its mandate, the BEA has developed a complex assignment system based principally on commuting flows between regions of the Country. This assignment system works well for the centralized population centers that characterize the majority of the U.S. economy. However, the BEA system is less effective at reflecting the economic organization of rural areas, where there is little interregional commuting. To address this problem, the BEA has developed a practice of using newspaper circulation data as a proxy for economic organization. In this thesis I develop a partial set model of regional economic organization based on the mathematics of fuzzy logic and propose it as a superior alternative to the BEAs method.

In this paper, we purpose a kernel-based fuzzy clustering algorithm to cluster data patterns in the feature space. Our method uses kernel functions to project data from the original space into a high dimensional feature space, and data are divided into groups though their similarities in the feature space with an incremental clustering approach. After clustering, data patterns of the same cluster in the feature space are then grouped with an arbitrarily shaped boundary in the original space. As a result, clusters with arbitrary shapes are discovered in the original space. Clustering, which can be taken as unsupervised classification, has also been utilized in resolving classification problems. So, we extend our method to process the classification problems. By working in the high dimensional feature space where the data are expected to more separable, we can discover the inner structure of the data distribution. Therefore, our method has the advantage of dealing with new incoming data pattern efficiently. The effectiveness of our method is demonstrated in the experiment.

Due to increase global competitive pressure, shortened product life cycles and ease of imitation, firms must continue to innovate to maintain their competitiveness. Technological innovation has become the primary basis of productivity improvements, sales volume growth, and competitiveness of firms, especially for the high-tech companies. Thus, identification and evaluation of technologies from a variety of perspectives now play important roles in the effective technological sources management. Traditionally, technological innovation studies stressed single model or variable having effects on firm productivity and performance. However, the challenge for business environment is continually changing; single model or variable is not good enough to explain the overall impact of technological innovation. The most difficult aspect of technological innovation performance measurement is the identification of appropriate metrics and approaches that provide information concerning these facets. In this study, the researcher tried to develop a technological innovation performance measurement model and determine tangible and intangible factors from the systematical perspective. That is, technological innovation in its nature is multi-dimensional and multi-criteria. Furthermore, technology innovation performance measurement can be conceptualized as multi-criteria a complex problem which involves the simultaneous consideration of multiple quantitative and qualitative requirements. In this empirical study, the researcher firstly utilizes the Delphi technique to build a hierarchical network structure model for evaluating the technological innovation performance measurement of high tech firms. Secondly, analytic network process (ANP) was applied to determine the importance weights of each dimension and criterion while exists interdependencies among criteria within the same dimension. Thirdly, Non-additive fuzzy integral method was then applied for information fusion and calculates the synthetic performance on a hierarchical network model structure for which criteria are interdependent and interactive. This study applied fuzzy measure and non-additive fuzzy integral method to derive the synthetic performance values of each dimension and firm. Through the technological innovation performance evaluation model can provide firms with an overview of their strengths and weaknesses with regards to technological innovation management. Furthermore, R&D managers and senior managers can apply this model to evaluate and determine the technological innovation capabilities of a firm to improve its technological innovation performance. Finally, this model may provide the useful information for managers and to reduce the overall technological innovation uncertainty.

Taiwan stock market exists a special feature that over 80% of participants are natural persons while only 20% are legal persons. Compared to the latter, natural persons own less expertise in stock trading. Thus the effectiveness of the local stock market is an interesting subject for research. In this paper, we will try to find out an answer through the using of technical analysis on the past two years trading data to see if it can gain benefit in investment.Most of the similar research in past exist some problems, which either use only single or a pair of technical indices for prediction, predict only a specific stock, or filter out unwanted training and testing data in preprocessing, etc. Thus their results may not really reflect the effectiveness of the market. In this paper, we will adopt a different way of experiment design to conduct the test.Past research has shown that a fuzzy decision tree outperforms a normal crisp decision tree in data classification when there are numerical attributes in the target domain to be classified (Y.M. Jeng, 1993). Since most of the technical indices are expressed in terms of numerical values, we therefore choose it as the tool to generate rules from the eight largest stocks out of the local stock market that have the largest capitals and highest turnover rate. The trees are evaluated with more objective criteria and used to predict the up or down of the stock prices in the next day. The experimental results show that the created fuzzy trees have a better predictive accuracy than a random walk, and the investment rewards based on the trees are much better than the buy-and- hold policy.