O presente trabalho aborda os principais conceitos relacionados com as redes neurais artificiais, que são modelos baseados no comportamento do cérebro. As redes neurais artificiais assemelham-se ao cérebro quanto à obtenção do conhecimento através de um processo de aprendizado, e quanto ao uso da força de conexão interneurônio para armazenar o conhecimento, conhecida como peso sináptico. Existem vários modelos de redes neurais artificiais; os principais modelos são abordados neste trabalho. Estes modelos diferem-se quanto à arquitetura e processo de aprendizado. A escolha do processo de aprendizado é influenciada pela tarefa a ser realizada pela rede neural. Cada modelo de rede neural artificial é mais adequado para resolver um determinado tipo de problema. Sugerem-se alguns problemas na área de administração financeira para serem resolvidos pelo uso desta tecnologia, com a especificação de um determinado modelo, e ainda se propõe uma contribuição para um assunto específico na área de administração financeira: o processo de previsão de insolvência das organizações. / The present work addresses the main concepts related to artificial neural networks, which are models based on brain behavior. Artificial neural networks resemble the brain regarding acquisition of knowledge through a learning process, and regarding the use of interneuron connection strength to storing the knowledge, known as synaptic weight. There are various artificial neural network models, the main models are addressed in this work. These models differ regarding the architecture and learning process. The choice of learning process is influenced by task to be carried out by the neural network. Each artificial neural network model is suitable for solving a determined type of problem. Some problems in the area of financial administration are suggested for resolution by the use of this technology, specifying a determinated model, and yet intend a contribution to a specific subject in the area of financial administration: the process of forecasting of insolvency of organizations.

<p>In this study, Artificial Neural Network (ANN) is used to check the integrity of the Global Positioning Recommended Minimum (GPRMC) sentences. The GPRMC sentences are the most common sentences transmitted by the Global Positioning System (GPS) devices. This sentence contains nearly every thing a GPS application needs. The data integrity is compared on the basis of the classification accuracy and the minimum error obtained using the ANN. The ANN requires data to be presented in a certain format supported by the learning process of the network. Therefore a certain amount of data processing is needed before training patterns are presented to the network. The data pre processing is done by the design and development of different algorithms in C# using Visual Studio.Net 2003. This study uses the BackPropagation (BP) feed forward multilayer ANN algorithm with the learning rate and the momentum as its parameters. The results are analyzed based on different ANN architectures, classification accuracy, Sum of Square Error (SSE), variables sensitivity analysis and training graph. The best obtained ANN architecture shows a good performance with the selection classification of 96.79 % and the selection sum of square error 0.2022. This study uses the ANN tool Trajan 6.0 Demonstrator.</p>

The greening of air transport is the driver for developing technologies to reduce the environmental impact of aviation with the aim of halving the amount of carbon dioxide (COଶ) emitted by air transport, cutting specific emissions of nitrogen oxides (NO୶) by 80% and halving perceived noise by the year 2020. Fuel Cells (FC) play an important role in the new power generation field as inherently clean, efficient and reliable source of power especially when comparing with the traditional fossil-fuel based technologies. The project investigates the feasibility of using an electric hybrid system consisting of a fuel cell and battery to power a small model aircraft (PiperCub J3). In order to meet the desired power requirements at different phases of flight efficiently, a simulation model of the complete system was first developed, consisting of a Proton Exchange Membrane hybrid fuel cell system, 6DoF aircraft model and neural network based controller. The system was then integrated in one simulation environment to run in real-time and finally was also tested in hardware-in-the-loop with real-time control. The control strategy developed is based on a neural network model identification technique; specifically Model Reference Control (MRC), since neural network is well suited to nonlinear systems. To meet the power demands at different phases of flight, the controller controls the battery current and rate of charging/discharging. Three case studies were used to validate and assess the performance of the hybrid system: battery fully charged (high SOC), worst case scenario and taking into account the external factors such as wind speeds and wind direction. In addition, the performance of the Artificial Neural Network Controller was compared to that of a Fuzzy Logic controller. In all cases the fuel cell act as the main power source for the PiperCub J3 aircraft. The tests were carried-out in both simulation and hardware-in-the-loop.

In this thesis, behavior learning and generation models are proposed for simple and complex behaviors of robots using unsupervised learning methods. Simple behaviors are modeled by simple-behavior learning model (SBLM) and complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models have common phases named behavior categorization, behavior modeling, and behavior generation. Sensory data are categorized using correlation based adaptive resonance theory network that generates motion primitives corresponding to robot&#039 / s base abilities in the categorization phase. In the modeling phase, Behavior-HMM, a modified version of hidden Markov model, is used to model the relationships among the motion primitives in a finite state stochastic network. In addition, a motion generator which is an artificial neural network is trained for each motion primitive to learn essential robot motor commands. In the generation phase, desired task is presented as a target observation and the model generates corresponding motion primitive sequence. Then, these motion primitives are executed successively by the motion generators which are specifically trained for the corresponding motion primitives. The models are not proposed for one specific behavior, but are intended to be bases for all behaviors. CBLM enhances learning capabilities by integrating previously learned behaviors hierarchically. Hence, new behaviors can take advantage of already discovered behaviors. The proposed models are tested on a robot simulator and the experiments showed that simple and complex-behavior learning models can generate requested behaviors effectively.

Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural network (BIANN), which is not only biologically meaningful, but also computationally powerful. The BIANN can serve as a novel computational intelligence tool in monitoring, modeling and control of the power systems. A comprehensive survey of ANNs applications in power system is presented. It is shown that novel types of reservoir-computing-based ANNs, such as echo state networks (ESNs) and liquid state machines (LSMs), have stronger modeling capability than conventional ANNs. The feasibility of using ESNs as modeling and control tools is further investigated in two specific power system applications, namely, power system nonlinear load modeling for true load harmonic prediction and the closed-loop control of active filters for power quality assessment and enhancement. It is shown that in both applications, ESNs are capable of providing satisfactory performances with low computational requirements. A novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. A comprehensive survey of the spiking models of living neurons as well as the coding approaches is presented to review the state-of-the-art in BIANN research. The proposed BIANNs are based on spiking models of living neurons with adoption of reservoir-computing approaches. It is shown that the proposed BIANNs have strong modeling capability and low computational requirements, which makes it a perfect candidate for online monitoring and control applications in power systems. BIANN-based modeling and control techniques are also proposed for power system applications. The proposed modeling and control schemes are validated for the modeling and control of a generator in a single-machine infinite-bus system under various operating conditions and disturbances. It is shown that the proposed BIANN-based technique can provide better control of the power system to enhance its reliability and tolerance to disturbances. To sum up, a novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. It is clearly shown that the proposed BIANN-based modeling and control schemes can provide faster and more accurate control for power system applications. The conclusions, the recommendations for future research, as well as the major contributions of this research are presented at the end.

In this study, Artificial Neural Network (ANN) is used to check the integrity of the Global Positioning Recommended Minimum (GPRMC) sentences. The GPRMC sentences are the most common sentences transmitted by the Global Positioning System (GPS) devices. This sentence contains nearly every thing a GPS application needs. The data integrity is compared on the basis of the classification accuracy and the minimum error obtained using the ANN. The ANN requires data to be presented in a certain format supported by the learning process of the network. Therefore a certain amount of data processing is needed before training patterns are presented to the network. The data pre processing is done by the design and development of different algorithms in C# using Visual Studio.Net 2003. This study uses the BackPropagation (BP) feed forward multilayer ANN algorithm with the learning rate and the momentum as its parameters. The results are analyzed based on different ANN architectures, classification accuracy, Sum of Square Error (SSE), variables sensitivity analysis and training graph. The best obtained ANN architecture shows a good performance with the selection classification of 96.79 % and the selection sum of square error 0.2022. This study uses the ANN tool Trajan 6.0 Demonstrator.

Power systems all over the world advance towards the direction of deregulation in the past few years. Introducing competition mechanism and the principle of market rules in deregulation. Utility companies will face unprecedented changes and challenges. Taiwan power company is also working on the deregulation direction with a competitive environment opened up, it will improve the scientific and technological levels and the service quality of electricity. Load management functions as the marginal price of electricity is predicted. Consumers can get Real-Time Pricing information determine their own buying strategy. One most representative deregulation example in U.S.A. is the PJM(Pennsylvania¡BNew Jersey¡BMaryland)system combining generating, transmitting, distribution and sales of electricity. It offers the information of real-time power supply and is one of the cases in the world. Historical data in the thesis comes from PJM. Artificial Neural Network was designed to the Locational Marginal Price(LMP), considering the factors such as temperature and other relevant data from deregulation with the introduction of various parameters in forecasting, and the use of week as a counting base. LMP will be forecasted. The forecasted results will be to check the accuracy and performance with initial data.

In this thesis the main objective is to design a machine vision system that classifies aflatoxin contaminated chili peppers from uncontaminated ones in a rapid and non-destructive manner via hyperspectral imaging and machine learning techniques. Hyperspectral image series of chili pepper samples collected from different regions of Turkey have been acquired under halogen and UV illuminations. A novel feature set based on quantized absolute difference of consecutive spectral band features is proposed. Spectral band energies along with absolute difference energies of the consecutive spectral bands are utilized as features and compared with other feature extraction methods such as Teager energy operator and 2D wavelet Linear Discriminant Bases (2D-LDB). For feature selection, Fisher discrimination power, information theoretic Minimum Redundancy Maximum Relevance (mRMR) method and proposed Multi Layer Perceptron (MLP) based feature selection schemes are utilized.Finally, Linear Discriminant Classifier (LDC), Support Vector Machines (SVM) and MLP are used as classifiers. It is observed that MLP outperforms other learning models in terms of predictor performance. We verified the performance and robustness of our proposed methods on different real world datasets. It is suggested that to achieve high classification accuracy and predictor robustness, a machine vision system with halogen excitation and quantized absolute difference of consecutive spectral band features should be utilized.

The rapid growth of the Internet has shaped up the global economy. The stock market information is thus more and more transparent. Although the investors can get more helpful information to judge future trend of the stock market, they may get wrong judgments because the stock market data are too huge to be completely analyzed. Therefore, the purpose of this study is to develop an artificial stock market analyst by employing the information technology with high speed and performance, as well as integrating the artificial intelligence techniques. We exploit case-based reasoning to simulate the analysts in using history stock market data, employ the artificial neural network to imitate the analysts in analyzing the macrofactors of stock market, and apply the fuzzy logic to humanize the artificial stock market analyst in making judgments close to the real stock market analysts. The artificial stock market analyst would use the modified case-based reasoning system combined with the artificial neural network, and incorporate the designed membership functions for macrofactors of stock market. We expect the system to improve the accuracy of Taiwan electric stock price prediction by applying macrofactors from the technical analysis indicators and financial crisis factors, and make better stock trading strategies.

現今台灣地區使用的地籍坐標系統有相當多種，在這當中最廣泛使用的為TWD67與TWD97坐標系統。由於不同時期建置的資料具有不同的地籍坐標系統，因此常需要在兩地籍坐標系統間進行坐標轉換。目前，國內正積極將地籍資料由TWD67坐標系統轉換為TWD97坐標系統。而如何在TWD67與TWD97之間進行坐標轉換，整合不同地籍坐標系統間資料之聯繫與共享，一直是國內學者致力於研究的問題。在廣泛的討論當中，最常使用的方式為利用最小二乘法求解轉換參數。 近幾年來由於神經網路技術快速的發展，提供了我們在進行地籍坐標轉換研究時新的選擇。本研究目的在於嘗試利用神經網路方式進行TWD67與TWD97地籍坐標系統；同時為了提升神經網路的效用，及解決神經網路的黑盒子問題，本研究提出利用神經網路建構網格式地籍坐標轉換模式的方法。為了驗証本研究所提出之坐標轉換方法，利用三個大小不同的實驗區之共同點資料，由不同方式轉換所得的結果顯示，以純粹利用神經網路方式所得轉換結果為佳，而網格式地籍坐標轉換模式所得結果與利用最小二乘法求解結果不相上下。 / Currently, there are two cadastral coordinate systems used in Taiwan. They are TWD67 (Taiwan Datum 1967) and TWD97 (Taiwan Datum 1997) cadastral coordinate systems respectively. Frequently it is necessary to transform from one coordinate system to another. One of the most widely used method is Least-Squares with affine transformations. The artificial neural network (ANN) provides a new technology for cadastral coordinate transformation. The popularity of this methodology is rapidly growing. The greatest advantage of ANN is that it can be used very successfully with a huge quantity of data and free-model estimation that traditional transformation methods cannot be applied. In this research coordinate transformation between TWD67 and TWD97 with artificial neural network (ANN) and Least-Squares with affine transformations were examined. Besides, in order to overcome the so called ‘‘Black Box Problem’’ of ANN, algorithm of applying artificial neural network to develop regional grid-based cadastral coordinate transformation model was proposed. Three data sets with varied sizes from the Taiwan region are used to test the proposed algorithms. The test results show that the coordinate transformation accuracies using the ANN models are better than those of using other methods, such as, Least-Squares with affine transformations. The proposed algorithms and the detailed test results are presented in this paper.