Made available in DSpace on 2016-04-29T14:23:53Z (GMT). No. of bitstreams: 1 Marcus Vinicius Cardador Francisco.pdf: 1267870 bytes, checksum: 9cad63d4aeb66e7ee5b764e9855e2b06 (MD5) Previous issue date: 2009-12-15 / This research has as goal to introduce an alternative solution for vehicles traffic flow control. Researches on similar subjects around the world were taken as a basement for this study which makes use of a hybrid architecture. This architecture is composed by a back-propagation algorithm, which is responsible for creating and training the networks that will take care of traffic flow control, and a genetic algorithm, responsible for all chromosome relations which will generate new networks based on its previews parents. The results for this combined algorithms shows that errors were decreased if compared to the other researches described below. This makes this a plausible solution. The whole complexity involved on current study as well as on traffic flow control gives many possibilities for development of new solutions and improvements on traffic flow subject / O objetivo deste trabalho é prover uma solução alternativa para o gerenciamento de fluxos de tráfego por meio de Redes Neurais. Pesquisas em diferentes partes do mundo dentro de um mesmo âmbito foram analisadas e forneceram uma base concreta para o corrente estudo que utiliza uma arquitetura híbrida. Essa arquitetura é composta por um algoritmo de propagação reversa com a finalidade de criar e treinar as redes destinadas ao gerenciamento dos fluxos de tráfego e por um algoritmo genético incumbido de realizar cruzamentos entre as redes anteriormente geradas em busca de novas redes a partir de suas sucessoras. Os resultados obtidos pela combinação dos algoritmos apresentam, de forma constante, valores de erros inferiores aos dos estudos analisados, tornado-a uma alternativa plausível. A complexidade envolta no presente estudo, bem como nos fluxos de tráfego, abre espaço para o desenvolvimento de novos trabalhos e projetos no âmbito de soluções e melhorias para sistemas de tráfego

Cloud computing is one important direction of current advanced technology trends, which is dominating the industry in many aspects. These days Cloud computing has become an intense battlefield of many big technology companies, whoever can win this war can have a very high potential to rule the next generation of technologies. From a technical point of view, Cloud computing is classified into three different categories, each can provide different crucial services to users: Infrastructure (Hardware) as a Service (IaaS), Software as a Service (SaaS), and Platform as a Service (PaaS). Normally, the standard measurements for cloud computing reliability level is based on two approaches: Service Level Agreements (SLAs) and Quality of Service (QoS). This thesis will focus on IaaS cloud systems’ Error Event Logs as an aspect of QoS in IaaS cloud reliability. To have a better view, basically, IaaS is a derivation of the traditional virtualization system where multiple virtual machines (VMs) with different Operating System (OS) platforms, are run solely on one physical machine (PM) that has enough computational power. The PM will play the role of the host machine in cloud computing, and the VMs will play the role as the guest machines in cloud computing. Due to the lack of fully access to the complete real cloud system, this thesis will investigate the technical reliability level of IaaS cloud through simulated virtualization system. By collecting and analyzing the event logs generated from the virtualization system, we can have a general overview of the system’s technical reliability level based on number of error events occur in the system. Then, these events will be used on neural network time series model to detect the system failure events’ pattern, as well as predict the next error event that is going to occur in the virtualization system.

Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. A general T2FNN is computational intensive due to the complexity of type 2 to type 1 reduction. Therefore the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates can not be both negative. Further, due to variation of the initial MF parameters, i.e. the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search better-fit spread rate for uncertain means and near optimal learnings for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.

This research employs five predicting variables to implementing the market timing strategy. These five variables are E/P1, E/P2, B/M, CP and GM. The investment performances of market timing under a variety of investment horizons are examined. There are four different forecasting horizons, which are one-month, three-month, six-month, and twelve-month investment horizons. Both the linear approach and artificial neural networks are employed to forecasting the market. The artificial neural network is employed with a view to capture the non-linearity property embedded in the market. The results are summarized as follows. (1) Both the linearity and nonlinear approaches are able to outperform the market. According to the results of Cumby-Modest test, they do have the market timing ability. (2) In the simple regression models, the performance of CP is relatively well compared to those of other variables. (3) The correct prediction rate increases as the investment horizon increases. (4) The performance of the expanding window approach is on average inferior to that of the moving window approach. (5) In the simulations of timing abilities over the period of May, 1991 to December, 1997. The multiple regression models has the best performance for the cases of one-month, three-month, and six-month investment horizons. On the other hand, BP(1) has the best performance for the case of one-year investment horizon. Contents Chapter 1 Introduction ……………………………………… 1 1.1 Background……………………………………………………………. 1 1.2 Motivations and objectives…………………………………………….3 1.3 Thesis organization ………………………………………………….. 4 Chapter 2 Literature Review…………………………………6 2.1 Previous studies on market timing……………………………………. 6 2.2 Predicting variables…………………………………………………… 8 2.3 Artificial Neural Networks……………………………………………10 2.4 Back Propagation Neural Networks…………………………………..11 2.5 Applications of ANNs to financial fields………………….………….12 Chapter 3 Data and Methodology……………………….….15 3.1 Data………………………………………………………………..….15 3.2 Linear approaches to implementing market timing strategy……….…18 3.3 ANNs to implementing market timing strategy…………..…………..23 Chapter 4 Results on Timing Performance……………..…26 4.1 Performance of linear approach………………………………………26 4.2 Performance of ANNs………………………………………………...38 4.3 Performance evaluation……………………………………………….39 Chapter 5 Summary…………………………………………54 5.1 Conclusions……………………………………………………….….54 5.2 Future works…………………………………………………………55 Appendix……………………………………………………..56 References……………………………………………………57 / This research employs five predicting variables to implementing the market timing strategy. These five variables are E/P1, E/P2, B/M, CP and GM. The investment performances of market timing under a variety of investment horizons are examined. There are four different forecasting horizons, which are one-month, three-month, six-month, and twelve-month investment horizons. Both the linear approach and artificial neural networks are employed to forecasting the market. The artificial neural network is employed with a view to capture the non-linearity property embedded in the market. The results are summarized as follows. (1) Both the linearity and nonlinear approaches are able to outperform the market. According to the results of Cumby-Modest test, they do have the market timing ability. (2) In the simple regression models, the performance of CP is relatively well compared to those of other variables. (3) The correct prediction rate increases as the investment horizon increases. (4) The performance of the expanding window approach is on average inferior to that of the moving window approach. (5) In the simulations of timing abilities over the period of May, 1991 to December, 1997. The multiple regression models has the best performance for the cases of one-month, three-month, and six-month investment horizons. On the other hand, BP(1) has the best performance for the case of one-year investment horizon.

在資訊來源日趨複雜化及多樣化之下，過多不必要的資訊反而造成決策上的困擾，因此資訊的篩選(Information Filtering)便成為設計資訊系統時所要考量的重要因素之一。資訊的有效篩選不僅使得決策的不確定性降低，同時讓決策人員能夠專注於對決策有重要影響的因素上，提高了決策的效率與品質。本研究即是以逆傳遞(Back Propagation)類神經網路模式(Artificial Neural Network Model)為基礎，設計一個能夠篩選出重要屬性的通用演算法；此演算法能夠幫助使用者去除一些對決策較無影響的屬性，讓使用者能夠減少資訊收集成本，並針對重要屬性做決策上的考量。同時在本研究中，我們還將此演算法應用在生產現場的屬性篩選上，幫助排程人員找出對於排程法則選取有重要影響的屬性；並藉此驗證篩選演算法的正確性及完整性。 / To screen the mformation effectively can improve the efficiency and quality of decision making dramatically. Since it does not only decrease the uncertainty of decision maldng, but also let decision makers can emphasize on the important factors which can significantly affect the result of decision. In this thesis we present an algorithm to find the important factors out based on the technique of back propagation neural network model. This algorithm can help users to remove some attributes which do not or seldom affect the result of decision, and let them can reduce the cost of data collection and emphasize on consideration of the remaining important attributes. And in this thesis, we also apply the algorithm to filter out the important production attributes of shop floor scheduling system which can significantly affect the selection of shop floor scheduling rules, and use the result of this experiment to verify the correctness and completeness of the algorithm.

A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to be construct. It should be noticed, however, that the present approach depends highly on the training algorithm of the FNN. Unfortunately, the available training methods; such as, the original Back-propagation (BP) algorithm, encounter many deficiencies when applied to matrix algebra problems; e. g., slow convergence due to improper choice of learning rates (LR). Thus, this study will focus on the development of new efficient and accurate FNN training methods. One improvement suggested to alleviate the problem of LR choice is the use of a line search with steepest descent method; namely, bracketing with golden section method. This provides an optimal LR as training progresses. Another improvement proposed in this study is the use of conjugate gradient (CG) methods to speed up the training process of the neural network. The computational feasibility of these methods is assessed on two matrix problems; namely, the LU-decomposition of both band and square ill-conditioned unsymmetric matrices and the inversion of square ill-conditioned unsymmetric matrices. In this study, two performance indexes have been considered; namely, learning speed and convergence accuracy. Extensive computer simulations have been carried out using the following training methods: steepest descent with line search (SDLS) method, conventional back propagation (BP) algorithm, and conjugate gradient (CG) methods; specifically, Fletcher Reeves conjugate gradient (CGFR) method and Polak Ribiere conjugate gradient (CGPR) method. The performance comparisons between these minimization methods have demonstrated that the CG training methods give better convergence accuracy and are by far the superior with respect to learning time; they offer speed-ups of anything between 3 and 4 over SDLS depending on the severity of the error goal chosen and the size of the problem. Furthermore, when using Powell's restart criteria with the CG methods, the problem of wrong convergence directions usually encountered in pure CG learning methods is alleviated. In general, CG methods with restarts have shown the best performance among all other methods in training the FNN for LU-decomposition and matrix inversion. Consequently, it is concluded that CG methods are good candidates for training FNN of matrix computations, in particular, Polak-Ribidre conjugate gradient method with Powell's restart criteria.

In this dissertation, it is aimed to develop an approach for the trip distribution element which is one of the important phases of four-step travel demand modelling. The trip distribution problem using back-propagation artificial neural networks has been researched in a limited number of studies and, in a critically evaluated study it has been concluded that the artificial neural networks underperform when compared to the traditional models. The underperformance of back-propagation artificial neural networks appears to be due to the thresholding the linearly combined inputs from the input layer in the hidden layer as well as thresholding the linearly combined outputs from the hidden layer in the output layer. In the proposed neural trip distribution model, it is attempted not to threshold the linearly combined outputs from the hidden layer in the output layer. Thus, in this approach, linearly combined iv inputs are activated in the hidden layer as in most neural networks and the neuron in the output layer is used as a summation unit in contrast to other neural networks. When this developed neural trip distribution model is compared with various approaches as modular, gravity and back-propagation neural models, it has been found that reliable trip distribution predictions are obtained.

Thesis (M.S.) -- Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

Thesis (Ph.D.) -- Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Made available in DSpace on 2015-04-22T22:00:46Z (GMT). No. of bitstreams: 1 Pamela Campos Levy.pdf: 4863540 bytes, checksum: 820e34768b005399acf73dec3e491ae5 (MD5) Previous issue date: 2012-08-24 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / Tuberculosis (TB) is an infectious disease transmitted by Koch's bacillus, or Mycobacterium tuberculosis. An estimated 1.4 million people died of tuberculosis in 2010. About 95% of these deaths occurred in developing countries, or development. In Brazil, each year are registered more than 68,000 new cases. Currently, Amazon is the Brazilian state with the highest incidence rate of the disease. a of TB diagnostic methods, adopted by the Ministry of Health is examining smear of bright field. The smear is the count of bacilli in slides containing sputum samples of the patient, prepared and stained according to the methodology standard. Over the past five years, research related to the recognition of bacilli tuberculosis, using images obtained by microscopy bright field, has been carried out with a view to automating this diagnostic method, given the fact that the number high smear tests performed by professional induce eyestrain and due to diagnostic errors. This paper presents a new method of recognition and targeting of tubercle bacilli in slides fields of images, containing pulmonary secretions of the patient, stained by Kinyoun method. From these bacilli images of pixels and background samples were extracted for training classifier. Images were automatically broken down into two groups, according with substantial content. The developed method selects an optimal set of color characteristics of the bacillus and of the background, using the method of selection climbing characteristics. These features were used in a pixel classifier, a multilayer perceptron, trained by backpropagation algorithm. The optimal set of features selected, {GI, Y-Cr, La, RG, a}, from the RGB color spaces, HSI, YCbCr and Lab, combined with the network perceptron with eighteen (18) neurons in first layer three (3) and the second one (1) in the third (18-3-1), resulted in an accuracy of 92.47% in the segmentation of bacilli. The image discrimination method in relation to automated background content contributed to affirm that the method described in this paper it is more appropriate to target bacilli images with low content density background (more uniform background). For future work, new techniques to remove noise present in images with high density of background content (containing background many artifacts) should be developed. / A tuberculose (TB) é uma doença infectocontagiosa, transmitida pelo bacilo de Koch, ou Mycobacterium tuberculosis. Estima-se que 1,4 milhões de pessoas morreram de tuberculose em 2010. Cerca de 95% dessas mortes ocorreram em países subdesenvolvidos ou em desenvolvimento. No Brasil, a cada ano são registrados mais de 68 mil novos casos. Atualmente, o Amazonas é o estado brasileiro com a maior taxa de incidência da doença. Um dos métodos de diagnóstico da TB, adotado pelo Ministério da Saúde, é o exame de baciloscopia de campo claro. A baciloscopia consiste na contagem dos bacilos em lâminas contendo amostras de escarro do paciente, preparadas e coradas de acordo com metodologia padronizada. Nos últimos cinco anos, pesquisas relacionadas ao reconhecimento de bacilos da tuberculose, utilizando imagens obtidas por microscopia de campo claro, tem sido realizadas com vistas a automatização desse método diagnóstico, em face do fato de que o número elevado de exames de baciloscopia realizado pelos profissionais induzirem a fadiga visual e em consequência a erros diagnósticos. Esse trabalho apresenta um novo método de reconhecimento e segmentação de bacilos da tuberculose em imagens de campos de lâminas, contendo secreção pulmonar do paciente, coradas pelo método de Kinyoun. A partir dessas imagens foram extraídas amostras de pixels de bacilos e de fundo para treinamento do classificador. As imagens foram automaticamente discriminadas em dois grupos, de acordo com o conteúdo de fundo. O método desenvolvido seleciona um conjunto ótimo de características de cor do bacilo e do fundo da imagem, empregando o método de seleção escalar de características. Essas características foram utilizadas em um classificador de pixels, um perceptron multicamada, treinado pelo algoritmo backpropagation. O conjunto ótimo de características selecionadas, {G-I, Y-Cr, L-a, R-G, a}, proveniente dos espaços de cores RGB, HSI, YCbCr e Lab, combinado com a rede perceptron com 18 (dezoito) neurônios na primeira camada, 3 (três) na segunda e 1 (um) na terceira (18-3-1), resultou em uma acurácia de 92,47% na segmentação dos bacilos. O método de discriminação de imagens em relação ao conteúdo de fundo automatizado contribuiu para afirmar que o método descrito neste trabalho é mais adequado para segmentar bacilos em imagens com baixa densidade de conteúdo de fundo (fundo mais uniforme). Para os trabalhos futuros, novas técnicas para remover os ruídos presentes em imagens com alta densidade de conteúdo de fundo (fundo contendo muitos artefatos) devem ser desenvolvidas.