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Aranibar, Luis Alfonso Quiroga. "Learning fuzzy logic from examples." Ohio : Ohio University, 1994. http://www.ohiolink.edu/etd/view.cgi?ohiou1176495652.
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Zhao, Mansuo. "Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization." University of Sydney. School of Electrical and Information Engineering, 2005. http://hdl.handle.net/2123/699.
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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.
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Zhao, Mansuo. "Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization." Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/699.
Full textAbstract:
Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.
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Muthu, Kavitha. "Expert system and fuzzy technique approaches to landslide hazard mapping." Thesis, University of Surrey, 2005. http://epubs.surrey.ac.uk/722/.
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Yan, Yongyi. "Fuzzy modus ponens deduction technique for activity analysis in network planning." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36075/1/36075_Yan_1998.pdf.
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A project is considered successful if it is completed within its scheduled duration and budgeted cost. To keep the project on schedule or budget, it is necessary for the project management to make an accurate forecast of project duration and cost to establish the pace of
performance and also to revise it at regular intervals because of the dynamic environment of the project. This paper presents a new method, AADSS (Activity Analysis Decision Support System), to help the planner in estimating the duration and the cost of construction activities. This model employs the Fuzzy Modus Ponens Deduction (FMPD) technique to evaluate the impact of different factors on activity duration and cost. A computer program developed based on the AADSS does not require the planner to understand the underlying concept of fuzzy logic and it is simple to use. Thus, the model fulfills its objective in comprehensively simulating the dynamic uncertainty variables and their impact on activity duration and cost estimates so that a more reliable project completion forecast can be made.
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Zhao, Jibo. "An Efficient Wide-Speed Direct Torque Control Based on Fuzzy Logic Technique." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1352922315.
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Chen, Zhibin. "Segmentation of MRI images using non parametric deformable models integrating fuzzy technique." Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001122.pdf.
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L'objectif de la thèse est de développer une méthode automatique pour segmenter les tissus cérébraux (la matière grise, la matière blanche et le liquide céphalo-rachidien) à partir des images IRM, fournissant ainsi des mesures quantitatives et précises du cerveau. Dans cette thèse, nous avons développé trois modèles déformables non-paramétriques en intégrant l'information statistique et l’information floue des images pour segmenter le cerveau en différents types de tissus. Nous présentons d'abord une méthode basée sur l’analyse de l'histogramme. La répartition de l'intensité des images est modélisée par le modèle de mélanges gaussiens (MMG). Les paramètres du MMG sont estimés par l’algorithme «Expectation Maximization». Ensuite, ils sont utilisés pour guider l'évolution des courbes pour atteindre la segmentation des tissus cérébraux. Nous proposons ensuite une amélioration d’un algorithme basé sur les contours actifs orientés région avec la contrainte géométrique. Grâce à la nouvelle expression proposée, il permet de résoudre le problème de stabilité sous-jacente associé à l'algorithme d’origine, et réalise une convergence rapide. Enfin, nous présentons une segmentation de multi-classes en intégrant une segmentation floue dans la méthode level sets. Elle utilise un ensemble d'équations différentielles ordinaires. Chacune d'elles représente une classe à segmenter. Cette approche réduit la complexité de calcul par rapport à l'algorithme multi-phase existant, permettant donc d’accélérer la vitesse de convergence. Toutes les méthodes ont été évaluées avec des images IRM simulées et réelles. Les analyses quantitatives sont données. Les résultats sont très encourageantsThe research goal of this thesis is to develop an automatic segmentation method to segment brain MRI images into different tissues (gray matter, white matter, and cerebrospinal fluid), providing quantitative and precise brain measurements. In this dissertation, we have developed three non-parametric deformable models integrating statistical information and fuzzy information of images to segment the brain into different tissue types from multi types of MRI images. We firstly present a histogram analysis based algorithm, where the intensity distribution of the MRI images is modeled via the mixture Gaussian model (MGM). The parameters of components in MGM are estimated via the Expectation Maximization (EM) algorithm. Then the estimated parameters are used to guide the evolution of the level set curves to achieve the brain tissue segmentation. We then propose an improved algorithm to region-based geometric active contour. Thanks to the new regional term, the new algorithm solves the underlying stability problem associated with the original algorithm, and achieves convergence with less iteration number compared with the original algorithm. Finally, we present a multiclass algorithm by integrating fuzzy segmentation with the level set methods. The algorithm uses a set of ordinary differential equations; each of them represents a class to be segmented. The multiclass algorithm reduces the computational complexity compared with the existing multiphase algorithm, so speeds up the convergence rate. All algorithms are evaluated with simulated and real MRI images, and quantitative analyses are provided. The results are very encouraging
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Casabayó, Bonàs Mònica. "Shopping behaviour forecasts : experiments based on a fuzzy learning technique in the Spanish food retailing industry." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/24209.
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The general aim of this thesis is to analyse the possibility of developing synergies when connecting 3 different areas of research namely consumer behaviour, market research and artificial intelligence (AI). The three areas of research are very extensive. When analysing the potential links between them, a wide number of triple combinations arise. In addition, the number of combinations can also be increased when applied to different industries but the food retailing industry is selected as the framework of this thesis. A general overview of the three disciplines is developed. Firstly, consumer behaviour fundamentals are interpreted and reconsidered from a food retailer’s perspective. This constitutes one approach to the research in this thesis. Secondary, considering that learning from past data to anticipate shopping behaviours is a retailer’s focus of research, an overview of the main market research forecasting models and techniques is carried out. Thirdly, machine learning (AI subfield) is also explained in respect of its capability to perform forecasting tasks. Handling customer data is not easy. Information tends to be ambiguous, uncertain and incomplete. Moreover, the customer behaves differently according to his/her situation. Another AI subfield, fuzzy logic (Zadeh 1965) is also explained as it copes with the concept of partial truth. Having reviewed the three disciplines, the triple combination of ‘shoppers (household)’, ‘forecasting behaviours’ and ‘fuzzy learning’ aspects from each mentioned domain respectively are selected as illustrates the scope of this thesis. The empirical research consists of two experiments focused on forecasting shopper’s behaviour (in particular household shopping behaviour), in the food retailing industry using LAMDA (a fuzzy learning technique).The methodology of research is mainly based on data extracted from a Spanish Food Retailer’s (Supermarcats Pujol SA) databases. The first experiment is based on LAMDA’s supervised learning approach and provides a model to forecast the current customers who are going to defect when a competitor opens a supermarket in the same area. The second experiment is based on LAMDA’s unsupervised learning approach and provides a model to forecast the current customers who are going to buy online once the company launches the Website. Results indicate that marketing expert’s judgements are a key point when using learning techniques to forecast behaviours. Customers are not simple robots. People may change their behaviour according to their situation. The results show that when applying the adequacy degree (fuzzy logic concept), the forecasting accuracy increases considerably.
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Anderson, Alastair Andrew 1956. "The representation of personal constructs as fuzzy subsets : developing a model and testing its efficacy." Monash University, Dept. of Management, 1999. http://arrow.monash.edu.au/hdl/1959.1/8592.
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Minotti, Cristiano. "Estimador fuzzy de velocidade para motores de indução trifásicos usando abordagem sensorless." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-15102008-135246/.
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O uso da tecnologia sensorless é uma tendência crescente para acionamentos industriais aplicados em máquinas elétricas. A estimação dos parâmetros elétricos e mecânicos envolvidos com o controle da máquina elétrica são utilizados freqüentemente para se evitar medir todas as variáveis envolvidas no processo. A redução de custos em acionamentos industriais, além do incremento da robustez do sistema, são algumas das vantagens do uso de técnicas sensorless. Este trabalho propõe o uso de lógica fuzzy para estimar a velocidade de rotação de motores de indução trifásicos. Estão presentes resultados de simulações computacionais e comparação com outras técnicas inteligentes para validação da abordagem apresentada.The use of sensorless technologies is an increasing tendency on industrial drives for electrical machines. The estimation of electrical and mechanical parameters involved with the electric machine control is used very frequently in order to avoid measurement of all variables from this process. The cost reduction may also be considered in industrial drives, besides the increasing robustness of the system, as advantages of the use of sensorless technologies. This work proposes the use of fuzzy logic to estimate the speed in three-phase induction motors. Simulation results are presented to validate the proposed approach and comparative analyses with other intelligent techniques are also outlined.
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Hester, Jesse Stuart. "A technique for determining viable military logistics support alternatives." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28274.
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Thesis (M. S.)--Aerospace Engineering, Georgia Institute of Technology, 2009.Committee Chair: Mavris, Dimitri; Committee Member: Fahringer, Philip; Committee Member: Nixon, Janel; Committee Member: Schrage, Daniel; Committee Member: Soban, Danielle; Committee Member: Vachtsevanos, George.
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Pigatto, André Vieira. "Desenvolvimento de um protótipo de sistema inteligente para análise da técnica de pedalada apresentada por ciclistas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/181808.
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Este trabalho apresenta o desenvolvimento de um sistema inteligente para análise da técnica de pedalada aplicada por ciclistas. Para isso, desenvolveu-se um par de pedais de encaixe instrumentados, a partir dos quais é possível medir a componente de força normal aplicada nas partes frontal e posterior dos pedais. O modelo virtual da célula de carga experimental foi desenvolvido através da digitalização dos pedais de encaixe comerciais, utilizando-se um sistema comercial de escaneamento 3D com precisão declarada de 0,1mm. Cada pedal foi instrumentado com oito extensômetros de resistência elétrica (HBM 1-LY-13-1.5/350). Posteriormente os carregamentos máximos em cada eixo de medida de força foram estabelecidos utilizando-se uma plataforma de aquisição comercial específica para medida de deformação mecânica. Considerando-se os valores determinados, desenvolveu-se o circuito de condicionamento e realizaram-se os ensaios de deformação estática, obtendo-se as funções de transferência de saída de tensão elétrica em função do carregamento mecânico. O erro de linearidade máximo, considerando todos os canais, ficou abaixo de 0,75% e a máxima incerteza expandida (k=2) por canal, obtida através da aplicação do método clássico, foi de 1,55%. Em sequência, integrouse o sistema de pedais desenvolvido a dois outros sistemas, são eles: um par pedivelas experimentais instrumentados, capazes de medir as três componentes da força aplicada aos pedais e transmitidas aos pedivelas com um erro de linearidade abaixo de 0,6% e uma incerteza combinada inferior a 3,22%, e um sistema de cinemetria comercial, cuja precisão declarada pelo fabricante é de 1mm. Para possibilitar uma comparação quantitativa entre treinos ou ciclistas, implementou-se um sistema inteligente, baseado em redes Neuro-Fuzzy (ANFIS). A partir dos valores da potência média, do desvio padrão da potência e da assimetria bilateral média, obtidos ao longo de ensaios realizados sob protocolo desenvolvido especificamente para este trabalho, um score que representa o nível da técnica de pedalada apresentado pelo ciclista é determinado. Com intuito de testar o sistema, desenvolveu-se um projeto de experimentos com 2 fatores controláveis (sujeito e nível de frenagem de um rolo de treinamento), e realizou-se ensaios com oito ciclistas de características fisiológicas e níveis de preparos distintos. Através da análise estatística, constatou-se que das 23 variáveis de resposta consideradas ao longo do experimento, 23 são influenciadas significativamente pelo fator controlado sujeito e oito são influenciadas significativamente pelo fator controlado nível de frenagem magnética.This report describes the development of an intelligent pedaling technique analysis system. To accomplish that, a pair of road bicycle pedals (SHIMANO R540) were instrumented to measure the forces that are applied to the front and back regions of the pedals. The virtual models of the pedals were developed based on a 3D scanned mesh developed with aid of a commercial 3D scanning system with a precision of 0.1mm. Each pedal was instrumented with eight electrical resistance strain-gages (HBM 1-LY-13-1.5/350). After that, the range of the mechanical deformation of each measurement channel was determined with aid of an industrial deformation acquisition system. The conditioning circuit was developed based on the mechanical deformation ranges previously determined and the static calibration experiment was performed to determine the voltage output transfer functions. The maximum linearity error determined per channel was 0,75% and the maximum expanded uncertainty (k=2), determined applying the classical methodology, was 1,55%. After that, the instrumented pedals developed were integrated with two complementary systems, which are: a pair of instrumented crank arm load cells which measure the components of the force applied to the bicycle pedal with a linearity error under 0.6% and an uncertainty of 3,22% and an Optitrack motion track system with a declared accuracy of 1mm. An intelligent pedaling technique analysis system was implemented through an Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the cyclist pedaling technique score based on three inputs: the average power applied to bicycle pedal, the average power standard deviation and the bilateral asymmetry index, all of them collected under an experimental protocol specifically designed for this application. To evaluate the behavior of the system developed a randomized block experiment design with two controlled factors was performed indoor with aid of an ergometer roll; 160 sprints were conducted with eight subjects of different training levels. From the data collected an ANOVA test was performed, which confirmed that all the 23 response variables vary significantly in function of the subject’s controlled factor and eight of them vary significantly in function of the magnetic braking level.
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Hu, Linlin. "A novel hybrid technique for short-term electricity price forecasting in deregulated electricity markets." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4498.
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Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting.
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Yadekar, Yaser. "A framework to manage uncertainties in cloud manufacturing environment." Thesis, Cranfield University, 2016. http://dspace.lib.cranfield.ac.uk/handle/1826/11776.
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This research project aims to develop a framework to manage uncertainty in cloud manufacturing for small and medium enterprises (SMEs). The framework includes a cloud manufacturing taxonomy; guidance to deal with uncertainty in cloud manufacturing, by providing a process to identify uncertainties; a detailed step-by-step approach to managing the uncertainties; a list of uncertainties; and response strategies to security and privacy uncertainties in cloud manufacturing. Additionally, an online assessment tool has been developed to implement the uncertainty management framework into a real life context. To fulfil the aim and objectives of the research, a comprehensive literature review was performed in order to understand the research aspects. Next, an uncertainty management technique was applied to identify, assess, and control uncertainties in cloud manufacturing. Two well-known approaches were used in the evaluation of the uncertainties in this research: Simple Multi-Attribute Rating Technique (SMART) to prioritise uncertainties; and a fuzzy rule-based system to quantify security and privacy uncertainties. Finally, the framework was embedded into an online assessment tool and validated through expert opinion and case studies. Results from this research are useful for both academia and industry in understanding aspects of cloud manufacturing. The main contribution is a framework that offers new insights for decisions makers on how to deal with uncertainty at adoption and implementation stages of cloud manufacturing. The research also introduced a novel cloud manufacturing taxonomy, a list of uncertainty factors, an assessment process to prioritise uncertainties and quantify security and privacy related uncertainties, and a knowledge base for providing recommendations and solutions.
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Bouguelid, Mohamed Saïd. "Contribution à l’application de la reconnaissance des formes et la théorie des possibilités au diagnostic adaptatif et prédictif des systèmes dynamiques." Reims, 2007. http://theses.univ-reims.fr/exl-doc/GED00000741.pdf.
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Les méthodes de Reconnaissance est l’ensemble des méthodes permettant de classifier des formes dans des classes. Nous avons choisi, parmi les méthodes de classifications existantes, la méthode possibiliste Fuzzy Pattern Matching (FPM) pour réaliser le diagnostic des systèmes dynamiques. FPM est simple et son temps de classification est constant et faible. De plus, elle est capable de sélectionner les sources d’informations les plus pertinentes et de traiter des données qui sont à la fois incertaines et imprécises. Cependant, FPM est une méthode de classification naïve, c'est-à-dire qu’elle classifie un nouveau point par la sélection d'une des décisions partielles. Chaque décision partielle est calculée pour chaque classe et par rapport à chaque attribut. FPM ne tient donc pas compte de la corrélation entre les attributs et considère la forme des classes comme convexe. Ces inconvénients rendent FPM inutilisable pour les applications réelles qui souvent nécessitent une discrimination non linéaire entre les classes. De plus, FPM n’est pas une méthode de classification adaptative ou prédictive. Elle ne peut pas extraire l’information manquante des points rejetés en quantifiant leur représentativité vis a vis des classes connues. Ces points, portent l’information sur l’apparition d’une nouvelle classe ou l’évolution entre deux classes. Les travaux de ce mémoire de thèse portent donc sur l’amélioration de la méthode FPM afin de remédier à ses limites. Les performances des solutions proposées sont illustrées à travers plusieurs exemples académiques et réelsThe problem of diagnosis by Pattern Recognition can be posed as a problem of classification, i. E. , the actual functioning mode can be determined by knowing the class of the actual pattern. We use the method Fuzzy Pattern Matching (FPM) to realize the diagnosis because it is a simple method based on a feature selection. In addition it has a small and constant classification time, and it takes into account both the imprecision and uncertainty. However FPM is marginal, i. E. , its global decision is based on the selection of one of the intermediate decisions. Each intermediate decision is based on one attribute. Thus, FPM does not take into account the correlation between attributes. Additionally, FPM considers the shape of classes as convex one. Also, FPM cannot realize the adaptive and predictive diagnosis because it rejects all the points which carry the information about the class evolution or the creation of a new class. These drawbacks make FPM unusable for many real world applications. In this thesis, we propose to improve FPM to solve these drawbacks. Several synthetic and real data sets are used to show the performances of the improved FPM with respect to classical one
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Birek, L. "Leakage forecasting with fuzzy evolving techniques." Thesis, Coventry University, 2016. http://curve.coventry.ac.uk/open/items/33903146-10c8-45d7-9867-79ed974edb10/1.
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The amount of water lost in various leakage events in water networks in the UK not only generates economic loses for the water management companies, but more importantly, threatens supplies to households and businesses in times of severe drought.
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Mahadevappa, Jyothi [Verfasser], Antonio [Akademischer Betreuer] Delgado, Antonio [Gutachter] Delgado, Cornelia [Gutachter] Rauh, and Eckhard [Gutachter] Flöter. "Fuzzy Logic Based Process Control Strategy Using an Inline Measurement Technique for Effective Sheeting of Wheat Dough in Small and Medium-Sized Enterprises (SMEs) / Jyothi Mahadevappa ; Gutachter: Antonio Delgado, Cornelia Rauh, Eckhard Flöter ; Betreuer: Antonio Delgado." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2020. http://d-nb.info/1207546216/34.
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Talwanga, Matiki. "The principle of inclusion-exclusion and möbius function as counting techniques in finite fuzzy subsets." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005227.
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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.
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Hussein, Sherif El-Sayed. "Utilising neuro-genetic techniques in standing and sitting." Thesis, University of Strathclyde, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273408.
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Liu, Xiaofeng. "Machinery fault diagnostics based on fuzzy measure and fuzzy integral data fusion techniques." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16456/1/Xiaofeng_Liu_Thesis.pdf.
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With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results.
Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively.
The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.
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Liu, Xiaofeng. "Machinery fault diagnostics based on fuzzy measure and fuzzy integral data fusion techniques." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16456/.
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With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results. Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively. The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.
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Ping, Hui. "Isolated word speech recognition using fuzzy neural techniques." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0019/MQ52633.pdf.
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Musikasuwan, Salang. "Novel fuzzy techniques for modelling human decision making." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/13161/.
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Standard (type-1) fuzzy sets were introduced to resemble human reasoning in its use of approximate information and uncertainty to generate decisions. Since knowledge can be expressed in a more natural by using fuzzy sets, many decision problems can be greatly simplified. However, standard type-1 fuzzy sets have limitations when it comes to modelling human decision making. In many applications involving the modelling of human decision making (expert systems) the more traditional membership functions do not provide a wide enough choice for the system developer. They are therefore missing an opportunity to produce simpler or better systems. The use of complex non-convex membership functions in the context of human decision making systems were investigated. It was demonstrated that non-convex membership functions are plausible, reasonable membership functions in the sense originally intended by Zadeh. All humans, including ‘experts’, exhibit variation in their decision making. To date, it has been an implicit assumption that expert systems, including fuzzy expert systems, should not exhibit such variation. Type-2 fuzzy sets feature membership functions that are themselves fuzzy sets. While type-2 fuzzy sets capture uncertainty by introducing a range of membership values associated with each value of the base variable, but they do not capture the notion of variability. To overcome this limitation of type-2 fuzzy sets, Garibaldi previously proposed the term ‘non-deterministic fuzzy reasoning’ in which variability is introduced into the membership functions of a fuzzy system through the use of random alterations to the parameters. In this thesis, this notion is extended and formalised through the introduction of a notion termed a non-stationary fuzzy set. The concept of random perturbations that can be used for generating these non-stationary fuzzy sets is proposed. The footprint of variation (FOV) is introduced to describe the area covering the range from the minimum to the maximum fuzzy sets which comprise the non-stationary fuzzy sets (this is similar to the footprint of uncertainty of type-2 sets). Basic operators, i.e. union, intersection and complement, for non-stationary fuzzy sets are also proposed. Proofs of properties of non-stationary fuzzy sets to satisfy the set theoretic laws are also given in this thesis. It can be observed that, firstly, a non-stationary fuzzy set is a collection of type-1 fuzzy sets in which there is an explicit, defined, relationship between the fuzzy sets. Specifically, each of the instantiations (individual type-1 sets) is derived by a perturbation of (making a small change to) a single underlying membership function. Secondly, a non-stationary fuzzy set does not have secondary membership functions, and secondary membership grades. Hence, there is no ‘direct’ equivalent to the embedded type-2 sets of a type-2 fuzzy sets. Lastly, the non-stationary inference process is quite different from type-2 inference, in that non-stationary inference is just a repeated type-1 inference. Several case studies have been carried out in this research. Experiments have been carried out to investigate the use of non-stationary fuzzy sets, and the relationship between non-stationary and interval type-2 fuzzy sets. The results from these experiments are compared with results produced by type-2 fuzzy systems. As an aside, experiments were carried out to investigate the effect of the number of tunable parameters on performance in type-1 and type-2 fuzzy systems. It was demonstrated that more tunable parameters can improve the performance of a non-singleton type-1 fuzzy system to be as good as or better than the equivalent type-2 fuzzy system. Taken as a whole, the techniques presented in this thesis represent a valuable addition to the tools available to a model designer for constructing fuzzy models of human decision making.
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Chou, Shen-Liang, and 周昇亮. "Using Multivariable Statistical Technique for Fuzzy Modeling." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/65519469491692133505.
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碩士國立臺灣大學
化學工程學研究所
87
This thesis aim at combining the PCA (principal component analysis)and PLS (partial least square) techniques with fuzzy modeling principle for developing a nonlinear modeling method. A simple method for generating a multiple-input/single-output crisp-type fuzzy model has been developed. Three steps are proposed in this identification method: the initial phase, the growing phase, and the refining phase. For a set of observed input/output data pairs, the structure and the parameters of a suitable fuzzy model with required accuracy can be determined by a series of algebraic computations, and nonlinear optimization procedure is not needed. However, this method has a weakness called "curse of high dimension" which exists in most fuzzy modeling method. The succeeding part of this article is to study the practicability of using mutivarable statistic technique to solve the " curse of high dimension ". It is found that PCA has the ability to combine dependent variabels to reduce dimension, but if irrelevant variable exists, PLS should be used. However, if the relation between input/output data is nonlinear, the nonlinear fuzzy model should be used with PLS. Several numerical example are supplied, demonstrating the effectiveness of using PCA/PLS and fuzzy model for identification of nonlinear relationship between variables.
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Chen, Po-Ting, and 陳柏廷. "A Hybrid Intrusion Detection Technique using Fuzzy Association Rules." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/73434706118062412029.
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碩士國立臺灣大學
電機工程學研究所
103
Intrusion detection includes both misuse detection and anomaly detection. Misuse detection concerns the detection of known attacks, while anomaly detection is about the detection of attacks that might be unknown. It is important for an intrusion detection system to have ability to detection both misuse and anomlay situations. The thesis presents an intrusion detection system (IDS) that architecture can achieve both misuse detection and anomaly detection. The goal of misuse detection is to achieve higher accuracy and anomaly detection to detect unknown attacks. The rule files can be edited and added to modify or expand the functionality. In this study, we use fuzzy association rule mining to automatically generate rule files for IDS. In this study, KDD Cup 99 dataset and our own dataset are for assessment and analysis. By using KDD Cup 99 dataset, the detection rate of misuse detection can reach almost 97.4% and the detection rate of anomaly detection can achieve 95% with false positive rate equal to 0%. Using our own dataset, the detection rate is 95% and the false positive rate is 10%.
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Chen, Jui-Cheng, and 陳瑞正. "Audio Classification and Segmentation Technique Using Fuzzy Neural Networks." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/10807119373540557017.
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碩士國立交通大學
電機與控制工程系所
93
In this thesis, we proposed an audio classification and segmentation system. The system is used to classify and segment audio files which contain silence, pure speech, pure music, and song according to their contents. We analyzed and compared features of audio signals and designed a two-stage classification flow to classify and segment input audio signals sequentially. The flow starts with the silence detection which indexes silence according to a threshold. Then, stage 1 classifies the nonsilence parts into pure speech and “with music components”. Stage 2 classifies the “with music components” parts in stage 1 into pure music and song. In order to solve the problem that traditional features do not work well when it comes to pure music/song classification, we proposed a novel feature named FVTP. The feature describes the property that variations of the spectrum structure are larger for song but smaller for pure music. Thus, the feature can improve the performance of pure music/song classification. On the other hand, an on-line self-constructing neural fuzzy inference network (SONFIN) was adopted as the main classifier in this system. The SONFIN finds its optimal structure as well as parameters automatically and it has a superior inference process. We achieved a better classification result by utilizing these properties. Experimental results showed that an accuracy rate of more than 90% was achieved. Thus, the proposed system is capable of being a front-end for many application systems such as speech recognition and speaker identification to improve the performance of these application systems.
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Huang, Mao-Ting, and 黃茂庭. "Implementation of PMSM Speed Control Using Fuzzy Tuning Technique." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/89510699118295182728.
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碩士國立成功大學
電機工程學系碩博士班
96
The object of this thesis is to realize the speed control of permanent magnetic synchronous machine (PMSM) using the self-tuning fuzzy technique. The control of state variables uses three phase mathematical transformation model to decouple abc states into q-axis and d-axis variables in synchronous rotating frame for field oriented control (FOC). Following that, we implement the algorithms of FOC, space vector pulse-width modulation (SVPWM), and speed control using the digital signal processor (DSP). Regarding to the influence of controller parameters on dynamic response of the motor speed, this thesis proposes the speed control of PMSM using self-tuning fuzzy technique. By the self-tuning technique, the on-line fuzzy logic controller (FLC) output can give better speed response under the load disturbances or variable speed commands. Testing results show the proposed control strategy is more robust than the traditional proportional-integral control.
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Yin, Huang Chiao, and 黃巧瑩. "HARQ Process for HSDPA by Fuzzy Q-learning Technique." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/15151366792629085484.
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碩士國立交通大學
電信工程系所
96
In order to provide higher speed and more effective downlink packet data service in 3G, high speed downlink packet access (HSDPA) is proposed by 3rd generation partnership project (3GPP). An important QoS requirement defined in spec for the hybrid automatic retransmission request (HARQ) process is to choose a suitable MCS to maintain the initial block error rate (BLER) smaller than 0.1 based on the channel quality information. In this thesis, we proposed a fuzzy Q-learning based HARQ (FQL-HARQ) scheme for HSDPA to solve this problem. The HARQ scheme is modeled as a Markov decision process (MDP). On one hand, the fuzzy rule is designed to maintain the BLER requirement by separated to different parts based on a shot term BLER performance. On the other hand, by considering both link adaptation and HARQ version, the Q-learning algorithm is used to learn the performance of MCS under different environment. After learning, we want to choose the MCS with highest throughput while not going to violate the BLER requirement. The simulation results show that the proposed scheme can indeed choose a suitable MCS for the initial transmission with channel information delay consideration. Comparing to other traditional schemes, the FQL-HARQ scheme can achieve higher system throughput and maintain the BLER at the same time.
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Ping-Hong, Chiang, and 蔣秉宏. "A New Framework for Watermarking Technique with Fuzzy Extraction." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/02252878798931098241.
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碩士輔仁大學
資訊工程學系
95
Because the growth of the digital multimedia causes many problems of copyright protection, watermarking techniques has been an important research field today. Watermarking techniques always use some characteristics of image which can embed the watermarks. They make some differences from the characteristics and these watermarks will be extracted by these differences. Our new framework for extraction also uses these characteristics. We map these differences into a fuzzy set and build a membership function. The other membership function will be produced by fuzzy noise detection. After building two membership functions, we construct a fuzzy inference system to promote the extraction ratio. At last, we pick up a watermark algorithm randomly to implement our new framework. The experimental result shows that our new framework has improved the extraction ratio effectively.
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Chiang, Pei Yu, and 江培瑜. "A Fuzzy Inference Anti-Shake Technique for Mobile Display." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/r267mm.
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Hotwani, Nikita. "Vibration Analysis of Faulty Beam using Fuzzy Logic Technique." Thesis, 2009. http://ethesis.nitrkl.ac.in/263/1/thesis_nikita.pdf.
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The method of detecting crack location and its intensity in beam structures by fuzzy logic techniques and using ALGOR for finite element analysis has been considered in this project. The fuzzy logic controller used here comprises of three input parameters and two output parameters. Gaussian and triangular member ship functions are used for the fuzzy controller. The input parameters to the fuzzy- Gaussian controller and fuzzy- triangular controller are relative deviation of first three natural frequencies. The output parameters of the fuzzy inference system are relative crack depth and relative crack location. At the beginning theoretical analyses have been outlined for cracked cantilever beam to calculate the vibration parameters such as natural frequencies. A set of boundary conditions are considered involving the effect of crack location. A series of fuzzy rules are derived from vibration parameters which are finally used for prediction of crack location and its intensity. The comparison is made between Gaussian and triangular membership functions by calculating deviation from expected values of crack depth and crack location. Then finite element analysis of cracked beam has been done using ALGOR software where input files have been given through designing software. The proposed approach has been verified by comparing with the results obtained from fuzzy logic technique and finite element analysis.
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Hotwani, Nikita. "Vibration Analysis of Faulty Beam using Fuzzy Logic Technique." Thesis, 2009. http://ethesis.nitrkl.ac.in/1061/1/thesis_nikita.pdf.
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The method of detecting crack location and its intensity in beam structures by fuzzy logic techniques and using ALGOR for finite element analysis has been considered in this project. The fuzzy logic controller used here comprises of three input parameters and two output parameters. Gaussian and triangular member ship functions are used for the fuzzy controller. The input parameters to the fuzzy- Gaussian controller and fuzzy- triangular controller are relative deviation of first three natural frequencies. The output parameters of the fuzzy inference system are relative crack depth and relative crack location. At the beginning theoretical analyses have been outlined for cracked cantilever beam to calculate the vibration parameters such as natural frequencies. A set of boundary conditions are considered involving the effect of crack location. A series of fuzzy rules are derived from vibration parameters which are finally used for prediction of crack location and its intensity. The comparison is made between Gaussian and triangular membership functions by calculating deviation from expected values of crack depth and crack location. Then finite element analysis of cracked beam has been done using ALGOR software where input files have been given through designing software. The proposed approach has been verified by comparing with the results obtained from fuzzy logic technique and finite element analysis.
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Yen, Ting-yu, and 顏廷諭. "on the fuzzy critical chain in project scheduling by using fuzzy quality function development technique." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/65264896921063937693.
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碩士國立成功大學
航空太空工程學系碩博士班
97
Project scheduling management has long received many interests, where uncertainties and unknown factors in environment often cause project schedule delay and/or extra cost. This work aims at the application of fuzzy quality function deployment (QFD) method to fuzzy critical chain scheduling. By considering several project uncertainties: budget, time limit, activity begin time, technology difficulty, manpower, and facility requirement, fuzzy QFD is to obtain the degree of fuzziness, which defines the fuzzy model and buffer time for project activities. Analytical results show that the project time obtained from the proposed method can be reduced by 15% as compared to PERT scheduling. Integration of fuzzy QFD and fuzzy critical chain method is shown to be effective and efficient for project scheduling management.
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CHIANG, HUNG-CHUN, and 江鴻鈞. "Elementary School Principal’s Leadership Competence Evaluation Indicators Based on Fuzzy Delphi Technique、Fuzzy Analytic Hierarchy Processand Fuzzy Multiple Assessment Method." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/11366839801496674550.
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博士國立臺中教育大學
教育學系
96
Abstract The confusion and complexity of educational leadership are a very harsh challenge to school principals. It takes a need of era to establish a set of suitable principal’s leadership competence evaluation indicators and to seek “similarity” within “difference.” This study uses the fuzzy Delphi method to integrate the opinions of experts to construct evaluation indicators, and then adopts the fuzzy analytic hierarchy process method to calculate the relative weights among each individual indicator so as to complete the construction of evaluation system. Because of the fuzziness of its essential features and subjective perception, it is usually inappropriate or not possible to express the evaluation values with clear-cut numerical values. By means of the fuzzy distribution and fuzzy multiple assessment method, this study explores the evaluation method which is different from added weight average calculation in the expectation that the principal’s leadership competence evaluation results can be more reasonable and all-encompassing. This study goes through the literature to analyze and consider the current educational conditions of elementary schools. The preliminary integration works out 4 grate orientations, 17 unclear abilities, and 118 ability indicators, 118 indicators are selected from the above-mentioned to be used as opinion questionnaire for the people having relationship with the policy. By means of the questionnaire, the opinions and views of the experts on the leadership competence evaluation indicators of the elementary school principals are collected. Those who are selected to offer their opinions include 9 scholars, 6 educational administrators, 9 elementary school principals and 8 teachers. All of the questionnaire papers are retrieved. The study tool used is “the changing society elementary school principal leadership competence evaluation indicators construction Delphi method investigation questionnaire” (the first stage), and “the changing society elementary school principal leadership competence evaluation indicators Delphi method relative weight investigation questionnaire.” ( the second stage) For the data analysis, APS Script of the Chinese edition operation system of the Microsoft Windows 2003 Serve +II S6.0 is used in the first stage. Then the self-developed evaluation indicator selection system on the web based on the EXCEL 2000 edition calculation function is used to figure out the triangular fuzzy numbers of the individual evaluation indicators from which the suitable evaluation indicators are selected. In the second stage, the hierarchy analytic method proposed by Buckley is used to proceed in related calculation in order to construct the weight system. In the third stage,” the changing society elementary school principal leadership competence evaluation investigation table” constructed in the first two stages is used to engage in the analyses and investigation of the three incumbent elementary school principals with fuzzy multiple assessment, fuzzy distribution, and fuzzy percentage evaluation method to reveal the evaluation results and to establish a set of systemized evaluation model. The followings are constructed as a result of this study: 1.The first-degree indicators include the four great orientations of ideology, attitude, recognition and technical skills. 2.The ideology orientations are divided into the four unclear abilities of the historical field of vision, global thinking, perspective insight and the innovative progress; attitude orientations are divided into four unclear abilities of active initiative, composed toleration, pressure adjustment and life-long study; technical skill orientations include the five nuclear abilities of administrative leadership, management strategy, time management, public relation and the prevention of crises; a total of 16 nuclear abilities which are the second degree indicators. 3.Each individual nuclear ability is developed separately into 2 to 7 detailed indicators for a total of 55 nuclear abilities, which are the third degree indicators. The evaluation results show that fuzzy multiple assessment method makes the principals get more information from the evaluation. Conclusions based on the study were as followed: Based on the conclusion of this study, the suggestions are proposed for the research methods, educational evaluation, educational administrative organization, elementary school principals and follow-up researches.
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Lee, Jia-Hao, and 李家豪. "A Trading Decision Support System Based on Neuro Fuzzy Technique." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/68891145861510207289.
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碩士靜宜大學
企業管理學系
89
This paper aims to construct a trading system by combining KD technical indexes and Neuro fuzzy technique. The data of the top 60 companies in Taiwan’s Stock market is collected for analysis. The empirical results show that traditional KD trading system could beat the market after considering the transaction cost. However, the proposed trading system based on the combination of KD technical indexes and neural fuzzy technique could significantly beat the traditional KD trading system and market. Besides, the trading system can perform better when short selling is allowed, indicating the ability of this proposed system to forecast the downward turning point. The derived portfolio by using Neuro fuzzy has a higher Sharpe ratio than that of the portfolio constructed according to the Markowitz asset allocation model, price earning ratio criteria and those of randomly selected portfolios.
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Wu, Kuan-Wei, and 吳冠緯. "Robust Adaptive Fuzzy Technique in Tower Crane Anti-Swing Control." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/787pjk.
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碩士大同大學
機械工程學系(所)
102
The 3-D tower crane is an underactuated mechanical system and it has been the research focus of dynamic modeling. An effective way to control the tower crane is proposed in this study for controlling the motion of the system. This include precise displacement and rotation of trolley motion and limited the payload swing due to perturbations. The proposed adaptive fuzzy technique applies a variable structure control (VSC) scheme in resolving the uncertainties upon operation of the tower crane. As a result, the payload swing can be limited to a randomly specified level and achieving the $H_\infty$ tracking performance. The proposed control algorithm eliminates losses due to drag, friction and uncertainty in system parameters. By applying a Lyapunov criterion and the Riccati inequality, the proposed algorithm guarantees all system states are uniformly ultimately bounded (UUB). Experiments were conducted to verify the accuracy of the proposed model. The proposed algorithm is shown to accurately predicted the motion of a tower crane under operation.
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Chang, Li-Hsing, and 張立興. "A Study of Image Sharpening Technique Based on Fuzzy Logic." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/yam6hf.
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碩士國立中興大學
資訊科學與工程學系
103
Due to image processing needs higher time and spatial complexity, it is difficult to implement and limited because of the hardware performance for the earlier period''s smart phone. But nowadays smart phone''s hardware specification is more powerful, and many post process of image processing is carried out on the smart phone. For example. HDR, Panoramic photo, Image sharpening. And so on. People uses digital image to records everything in their life. It is became our part of life. This study proposes a self-adaptive image sharpening method based on fuzzy logic. This method will increase the sharpening degree of high frequency''s area of image. (edge & detail).meanwhile it will decrease the degree of sharpening in the low frequency area of image. For increase execution speed, we used a simple way to judge if the position is located in the area of high frequency, If not, the value of pixel is output directly. As a previous sharpening method like Laplacian, using the linear method to get the effect of sharpening, but it will also increase the noise of the area of low frequency. However Unsharp Masking is to add back the computed high frequency image to the original image to get the sharpening image, it also increase the noise, to avoid the side-effect of noise increased, we offer a non-linear sharpening method and using with self-adaptive sharpening degree parameter to adjust, during the edge the sharpening effect is good and will not increase the noise in flat area. The experimental results show that this method effectively sharpen the edge and detail. The noise of flat area will not increase if Compare to traditional sharpening method Laplacian & Unsharp Masking.
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Wu, Yao-Ting, and 吳曜廷. "Face Recognition and Destiny Foreseeing by Using Fuzzy Classification Technique." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/87425209250827454844.
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碩士逢甲大學
自動控制工程所
98
This paper proposes a face recognition and fortunes foreseeing system for specified person by using fuzzy inference method. This system uses CCD camera to take a picture of specified person in the best distance, and uses a skin color detection method to find out the facial area by separating skin color scope. This achieves the purpose of first positioning. After the preliminary positioning , we locate the facial contour by using the ellipse template method. Find out the locations of eye and lip of the five sense organs in the human face, and then to get the complete shape for eye and lip separately by using image processing technique and morphology .In this research, we classify the sample template into some classes by using fuzzy classification rule in advance, this work will speed up to run the real-time jobs of face recognition, 3D face modeling and destiny foreseeing. Afterward, we apply the -Norm minimization criterion to calculate the certainty degree of recognized face and estimated destiny. Finally, we also infer the fortune foreseeing analysis based on the shapes of eye, lip and face as well as face feature recognition method.
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Wong, Hui-Lin, and 翁慧琳. "A Study of Marketing Applications Applying Fuzzy Data Mining Technique." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/10448259440164711441.
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碩士朝陽科技大學
資訊管理系碩士班
95
A huge amount of data has been accumulated in the enterprise databases due to the wide use of information systems today. An enterprise may have more competitive advantages if the useful information can be discovered from the collected data. Data mining thus is widely applied for the purpose. Previous studies, nevertheless, mine either the occurrence-associations of the products or the quantities of the associated items without considering the genuine concern of the enterprise – the profit. In this thesis, the numeric data that is central to the profit is transformed into semantically meaningful terms, using fuzzy techniques. Fuzzy data mining algorithms are proposed to solve the profit mining problems in both tourism and supermarket applications. Making recommendations for customers as added services may increase the competitive advantages of a tourism operator. Most association-rule mining algorithms are limited to one or two dimensional transactional data so that the results for recommendations might be unsatisfactory. A high quality tour recommendation should be generated by considering the places, times, durations, and sequences of tourism spots altogether. Therefore, a multi-dimensional sequential pattern mining algorithm with fuzzy semantics is proposed to discover effective tour recommendations in this study. The profit mining in supermarket applications is emphasized on the high-profit items. For items of high-profit rate and high-sale rate, a normalization technique is applied to extract the true weight and meaning of the item in proportion to the whole transaction in the aspects of costs and profits. The item-sets that generate the majority of profits for the enterprise and the outliers can be identified accordingly without bias. For items of high-profit rate but low-sale rate, a generic algorithm is applied to eliminate the subjective determination from human experiences. The parameters of the membership functions are trained using the genetic algorithm to generate the initial populations that evolve into the optimal solution for profitable items. The shapes are also adjusted by the generic algorithm on the basis of gross profits. Human errors can be minimized and the high profit item-sets, either high or low sale rate, can be discovered.
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Satheesh, Adireddi. "Navigation of Real Mobile Robot by Using Fuzzy Logic Technique." Thesis, 2015. http://ethesis.nitrkl.ac.in/7901/1/2015_MT_Navigation_Sateesh.pdf.
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Now a day’s robots play an important role many applications like medical, industrial, military, transportation etc. navigation of mobile robot is the primary issue in now a days. Navigation is the process of detection and avoiding the obstacles in the path and to reach the destination by taking the surrounding information from the sensors. The successful navigation of mobile robot means reaching the destination in short distance in short period by avoiding the obstacles in the path. For this, we are using fuzzy logic technique for the navigation of mobile robot. In this project, we build up the four-wheel mobile robot and simulation and experimental results are carried out in the lab. Comparison between the simulation and experimental results are done and are found to be in good.
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Chen, Ruey-Maw, and 陳瑞茂. "Multiprocessor Scheduling Problem Analysis Using Neural Networks and Fuzzy Clustering Technique." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/13761442872747569571.
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博士國立成功大學
工程科學系
88
Many applications involve the concept of scheduling. Most scheduling applications have been demonstrated as NP-complete problems. A variety of schemes are introduced in solving these scheduling applications, such as artificial intelligence, linear programming, neural networks, genetic algorithms, fuzzy logic, and so forth. This dissertation applies the extended 3-D Hopfield neural network, normalized mean field annealing technique and competitive Hopfield neural network, respectively, to resolve a multi-processor problem (known to be a NP-hard problem) with no process migration, constrained times (execution time and deadline) and limited resources. Additionally, a new approach of first analogizing a scheduling problem to a clustering problem and then using a fuzzy Hopfield neural network clustering technique to solve the scheduling problem is proposed. This investigation utilizes this new approach to demonstrate the feasibility of resolving a multiprocessor scheduling problem with no process migration and constrained times (execution time and deadline) and resources. The Hopfield neural network is extensively applied to obtaining an optimal/feasible solution in many different applications. Although providing rapid convergence to the solution, the system frequently converges to a local minimum. Stochastic simulated annealing is a highly effective means of obtaining an optimal solution capable of preventing the local minimum. This important feature is embedded into a Hopfield neural network to derive a new technique, i.e. mean field annealing. The normalized mean field annealing technique; a variant of mean field annealing; was conducted to simplify the neural network. A modified cooling procedure to accelerate reaching equilibrium (convergence) for normalized mean field annealing was applied in this study. Hopfield neural networks, although providing rapid convergence to the solution, require a laborious effort to determine coefficients. A competitive learning rule is capable of reducing the time-consuming effort of obtaining coefficients and provides a highly effective means of attaining a sound solution. Restated, the competitive mechanism simplifies the network complexity and help to overcome the scaling problem. This important feature is applied to Hopfield neural network to derive a new technique, i.e., competitive Hopfield neural network. In this dissertation, we utilize those neural network schemes to solve the scheduling problem. Simulation results demonstrate that the derived energy function worked effectively, and good and valid solutions for sophisticated scheduling instances can be obtained using those schemes. Fuzzy c-means clustering technique is possibly the best performing of all clustering algorithms. The fuzzy Hopfield neural network approach integrates fuzzy c-means clustering strategies into a Hopfield neural network. Intrinsically, fuzzy Hopfield neural network involves the competitive mechanism. Hence, neural network complexity is reduced. In this approach, the process and processor of the scheduling problem can be regarded as a data sample to be processed and the clusters after de-fuzzification, respectively. The scheduling results can be obtained using fuzzy Hopfield neural network clustering technique. Simulation results illustrate that imposing the fuzzy Hopfield neural network provides an appropriate approach for solving this class of scheduling problems.
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Tsai, Pei-Chun, and 蔡佩君. "Face detection and recognition based on fuzzy theory and neural technique." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/50182223129448794553.
Full textAbstract:
碩士義守大學
資訊管理學系碩士班
96
We develop and improve an algorithm in order to detect the faces and recognize theses identity in daily life images in the varied background. We use less-dimension vectors to reduce images complexity and improving interference with noise in images,increasing ability of face detection and recognition. The system of face detection and recognition is divided into three stages: face detection, face location, and face recognition. In the first stage, we use a fuzzy Gaussian classifier and a face feature extracting neural network to detecting faces in image. In this stage, we hope to divide images to face images and non-face images roughly by fuzzy Gaussian classifier. We compute the fuzzy Gaussian parameters of input images, and then accumulate the square errors of Gaussian parameters between training patterns to exclude the most part of non-face image. Next, we feed the passed images to the feature extracting neural network for detecting faces accurately. In the face location stage, we use Gaussian spread method to remove some fault detections in the previous detecting stage and locate the faces in images. In the last stage, we use a fuzzy c-means and a framework of parallel neural networks to recognize the faces that located in the previous stage. The fuzzy c-means can classify each input image to some clusters and activate their small-scale parallel neural networks corresponsivelyto recognize the input images. Our algorithm can reduce the dimension of images, and eliminate a great deal of non-face images by classifier. Therefore, we can decrease the training time and recognition efficiently. Further, we can promote the detection and recognition ability of complex face images accurately.
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Kumar, S., and V. Baranwal. "Speed Control of Separately Excited DC Motor using Neuro Fuzzy Technique." Thesis, 2010. http://ethesis.nitrkl.ac.in/1712/1/project_thesis_modofied.pdf.
Full textAbstract:
This paper uses NEURO FUZZY TECHNIQUE in estimating speed and controlling it for a separately excited DC motor. The rotor speed of the dc motor can be made to follow an arbitrarily selected trajectory. The purpose is to achieve accurate trajectory control of the speed of saperately excited DC Motor, especially when the motor and load parameters are unknown.
Such a neuro fuzzy control scheme consists of two parts. One is the neural identifier which is used to estimate the motor speed error (state error or state error derivative). The second is the fuzzy logic controller which is used to generate a control signal for a chopper & speed control of separately excited DC Motor.
The purpose of this technique is to achieve accurate trajectory control of the speed.
Such a control scheme consists of two parts. One is the neural identifier which is used to estimate the motor speed. The other is the neural fuzzy logic controller which is used to generate the control signal (fuzzy output).
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VERMA, SAMEER. "APPLICATION OF FUZZY TECHNIQUE FOR SUSTAINABILITY EVALUATION OF TRANSPORTATION CORRIDORS DURING CONSTRUCTION." Thesis, 2014. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15628.
Full textAbstract:
The most widely accepted concept of sustainability as defined during the Bruntland
Commission in 1987 is “meeting the needs of the present generation without
compromising the ability of future generations to meet their own needs”. The goals
of providing sustainable features in the design and construction of transportation corridor
in an Urban Environment are to minimise impacts on the environmental resources,
consumption of material resources, energy consumption, encourage the use of new and
innovative approaches, enhance the historic, scenic and aesthetic context and integrate
into the community in a way that helps to preserve and enhance community life,
encourage community involvement in the transportation planning process, encourage
integration of non-motorized means of transportation and finally find a balance between
what is important to the community, to the natural environment and is economically
sound. These 3 Parameters i.e. social, economic and environmental are most
commonly accepted as three pillars of sustainability.
In this research, Sustainability indicators of a transportation corridor during
construction in an urban environment have been identified and detailed out. The
research has been made on a 3.2 Kms long elevated road project under construction from
Vikaspuri to Meerabagh in West Delhi by PWD and Metro rail elevated corridor (part)
from Punjabi Bagh to Mayapuri as a part of phase 3, line 7 by DMRC. During the
research study made at both sites in the midst of the construction period, it was identified
that Sustainability of these transportation corridors during the construction stage is just
not limited to three Pillars, but in actually much beyond that. Every activity or any
Project is to be looked into right perspective to understand its relevance to all those it
matters. Transportation Corridor is a field which, during its operational stage, can affect
the life in every area varying from education sector, all kinds of commercial activities,
availing of medical amenities or say movement of the public at large for any purpose they
wish.
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It is not only the operation stage, but the construction stage also makes an impact on
the residents living nearby as well as on the commuters passing through the corridor
on the diverted route. Both these members of society are subjected to Air Pollution,
Noise Pollution, water pollution, and increase in travel time besides Health and Safety
concerns. Environment faces irreversible degradation besides other adverse impacts on
the number of directly or indirectly related issues.
Various Sustainability Indicators during the construction stage as identified for an
elevated transportation corridor and thereafter classified under various categories is
covered in this research.
A comparative study on the above mentioned sites (of PWD and DMRC), during
construction under identical urban environment, was conveyed to evaluate the
sustainability of these sites, using fuzzy logic, so that the site more sustainable based on
identified sustainability indicators is known. Methodology and result of the study are
also discussed in this research.
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Shu-Chen, Wu. "A Study of Neuro-Fuzzy Technique on Time Series Forecast Model Selection." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0005-1207200610431500.
Full text
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Ting, Chuang-Kuang, and 丁崇光. "The Load Balancing Dispatcher Using Fuzzy/ANFIS Technique in Heterogeneous Servers Environment." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/17512427757805700238.
Full textAbstract:
碩士國立交通大學
電信工程系
89
The Internet traffic increases rapidly, especially the World Wide Web traffic. For a Application Service Provider(ASP), there are many users connect with it at the same time. Because the ability of server is limited, it cannot deal with all users' requests at the same time. Then the congestion is happened. The best solution to solve this problem is to increase the number of servers to deal with the users' requests and to duplicate the contents of the server. Because of the cost, the ASP cannot increase the number of servers unlimitedly. Under the condition of limited number of servers, it should dispatch new sessions of users to the server according to load balance of servers to maximize the system capacity and then avoid congestion. In recent years, The intelligent techniques such as fuzzy logic, neural network, ANFIS architecture, have been widely applied to deal with traffic control. Most research results show that the intelligent techniques can have better performance than conventional schemes. Now we have an idea to slove this problem by using fuzzy system. When we know all information of the current state, we may predict the load in the next state in some way. But it is not accuracy. In this thesis, we analyse the system deeply and propose the fuzzy algorithm. The dispatcher depends on the information which is sent periodically by the servers in the server farm to make decisions. This algorithm considers the short term and long term server characteristics to make decisions. This can have better performance than all other conventional algorithms in both request packet loss probability and overall system utilization. Based on the same model, we also propose an ANFIS technique to find the optimum fuzzy solution. The ANFIS technique can derive the best fuzzy rule and membership functions dynamically. And the performance of fuzzy algorithm in request packet loss probability can be further improved.
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Su, Shaw-Hwa, and 蘇紹華. "DNA Sequence Analysis Based on Fuzzy Neural Network and Simulated Annealing Technique." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/01804767199317031064.
Full textAbstract:
碩士國立中正大學
電機工程研究所
82
Protein plays an important role in all kinds of animals. The whole procedure of protein synthesis includes conversions of DNA sequence to primary RNA, primary RNA sequence to mRNA, and finally mRNA sequence to protein. Usually, the nucleotide acids in a DNA sequence can be labelled into two different ca- tegories, i.e., the coding nucleotide acid and the noncoding nucleotide acid. Usually, in the protein synthesis process the noncoding regions in a DNA sequence have to be separated from the coding regions. Therefore, how to identify and remove the noncoding nucleotide acids in an unknown DNA sequence be- comes an important issue in the protein synthesis process. In the first part of this thesis, we propose a fuzzy neural network to deal with the above problem. The proposed approach takes the neighborhood information of a given nucleotide acid into account and the experimental results reflect that the method is superb. After all the DNA sites in a sequence have been identified, the next problem to be solved is to perform sequence alignment. In this thesis, a method which can perform simultaneous alignment on a large amount of sequences is presented. In this method, the simulated annealing technique is adopted to execute an opti- mization process. From the identified consensus sequence plus the calculation if the Shannon information clearly reflect that several sequence motives are conserved:(a) a guanosine rich re- gion, (b) the polypyrimidine tract.
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Wu, Shu-Chen, and 吳淑貞. "A Study of Neuro-Fuzzy Technique on Time Series Forecast Model Selection." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/25600126752472316835.
Full textAbstract:
碩士國立中興大學
行銷學系
94
Time-series forecasting techniques have been broadly used in various industries. However, selecting an appropriate model for a given time series may not be an easy task. Practitioners fit time series data to different models according to features of time series data on a try-and-error basis, and then select an appropriate model with a competitive performance. This strategy is costly and time-consuming when a large number of candidate models and/or time series are involved. In this study, we propose an intelligent model selecting approach based on the neuro-fuzzy technique. With this approach, a most appropriate model for a time series data can be identified during a reasonable short period. Decision tree techniques, neural networks techniques, discriminant analysis methods, and other meta-learning approaches have been proposed as formal tools for forecast model selection. Nevertheless, they are either constrained by statistical assumptions or lack of the ability to explicitly express the relationship between performance variable and explanatory variables. An expert system is an approach which can formalize knowledge for model selection. However, knowledge acquisition heavily relies on experts who may be expensive and scarce in numbers. In order to tackle the difficulty, the proposed neuro-fuzzy model in this study is trained by actual data to extract model selection knowledge expressed in the if-then rule form. The data of this study are certain magazine weekly sales in 2005 from 305 convenient chain stores in Taiwan. We investigate the problem of selecting the most appropriate forecast model among three popular models in practice.
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Chen, S. H., and 陳舜賢. "Fuzzy Rule Based Backpropagation Neural Network Constructed by Divide-and-Conquer Technique." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/21539678188308010459.
Full textAbstract:
碩士國立中興大學
電機工程學系
93
This thesis proposes a novel neural fuzzy network, the Fuzzy Rule Based Back-Propagation Neural Network (FRBPNN), constructed based on the divide-and-conquer technique. In fuzzy rule form, the FRBPNN is of Takagi-Sugeno-Kang (TSK)-type, where the consequence is a BPNN. The architecture design of FRBPNN employs the concept of fuzzy clustering that divides the input training data to different clusters, and the input-output mapping of each cluster is learned by a BPNN. In FRBPNN, once a new fuzzy rule is built, a corresponding BPNN will be built successively. Learning of FRBPNN is based on simultaneous structure and parameter learning. Initially, the rule base is blank. All of the rules are constructed on-line by fuzzy clustering. For a newly generated rule, a criterion is proposed to determine whether a new fuzzy set should be generated on each input variables. This way we can reduce the number of fuzzy sets. The structure of BPNN at the consequence of each new fuzzy rule can be assigned in advance or automatically built. For automatic building, the initial BPNN contains only one hidden node. When the learning error decreasing rate is not satisfied over a period of time, a new node is added to the BPNN in the rule with the largest distributed error. For parameter learning, all of the free parameters in the BPNN and precondition part of the fuzzy rules are learned by gradient descent. To measure the performance of FRBPNN, four examples are simulated. Performance of FRBPNN is compared with BPNN and other type of neural fuzzy network to verify its superiority.
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Yuh-Jiun, Penth, and 彭昱鈞. "Realization Study of Phase/Frequency-Locked Servo System by Fuzzy Control Technique-." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/58204938714234399363.
Full textAbstract:
碩士國立臺灣科技大學
電子工程技術研究所
86
In this paper, a fuzzy control-based phase/frequency-locked servosystem (FC-PFLS) is proposed to achieve the servo locking performancemore stable and reliable. A fuzzy-clustering-based genetic algorithm(FC-GA) is presented to optimize the locking behavior of the FC-PFLS. A genetic algorithm-based fuzzy pulse pump controller (GA-FPPC)whose membership functions and inference rules are constructed by FC-GA is exploited to provide an adaptive motional profile for the motor according to the detected phase/frequency error. The GA-FPPC canadaptively provide the acceleration, constant, and deceleration for the motor in a fast response just only with nearly zero overshoot and lesssteady-state error. The physical model and the linearized model of theFC-PFLS are built. Compare with the conventional pump controller, adaptive pulse pump controller (ADPC) and variable slope pulse pump controller (VSPPC), the experimental results show that the combination of the fuzzy logic control with the phase/frequency-locked servo technique can achieve high accuracy and fast motion responses for theservo mechanism. In comparison with the ADPC, the acquisition times of the presented GA-FPPC for the short, middle, and long distance servo are improved by 30%, 53% and 57%, respectively. For the VSPPC, the acquisition times are improved by 30%, 14%, and 0% , respectively.