<p>In this project a method to analyse images is presented. The images document the development of grassroots in a tilled field in order to study the movement of nitrate in the field. The final aim of the image analysis is to estimate the volume of dead and living roots in the soil. Since the roots and the soil have a broad and overlapping range of colours the fundamental problem is to find the roots in the images. Earlier methods for analysis of root images have used methods based on thresholds to extract the roots. To use a threshold the pixels of the object must have a unique range of colours separating them from the colour of the background, this is not the case for the images in this project. Instead the method uses a neural network to classify the individual pixels. In this paper a complete method to analyse images is presented and although the results are far from perfect, the method gives interesting results</p>

Thesis (doctoral)--Universität, Passau, 2000.

Šiame magistriniame darbe išanalizuota dirbtinių neuroninių tinklų teorija. Detaliai išnagrinėtas atgalinio klaidos sklidimo algoritmas. Pagal jį parašytos programos: C++ kalba ir Matlab sistemoje su siūlomais neuroninių tinklų konstravimo įrankiais. Lyginant programas atlikti tyrimai su irisų ir vyno atpažinimo duomenimis. Tyrimo metu ištirti ir paanalizuoti daugiasluoksniai neuroniniai tinklai su paslėptais vienu ir dviem sluoksniais. / This paper offers a profound research the theory of artificial neural network. It gives a deep analysis of error back propagation and provides error back propagation program written in C++ language and Matlab system with relevant neural network construction tools. To compare both programs I carried out research of wines recognition data and irises data. Analyzed feedforward neural network with hidden one and two layers.

This thesis focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances.

In this contributon we evaluate on-line and off-line techniques to train a single hidden layer neural network classifier with logistic hidden and softmax output transfer functions on a multispectral pixel-by-pixel classification problem. In contrast to current practice a multiple class cross-entropy error function has been chosen as the function to be minimized. The non-linear diffierential equations cannot be solved in closed form. To solve for a set of locally minimizing parameters we use the gradient descent technique for parameter updating based upon the backpropagation technique for evaluating the partial derivatives of the error function with respect to the parameter weights. Empirical evidence shows that on-line and epoch-based gradient descent backpropagation fail to converge within 100,000 iterations, due to the fixed step size. Batch gradient descent backpropagation training is superior in terms of learning speed and convergence behaviour. Stochastic epoch-based training tends to be slightly more effective than on-line and batch training in terms of generalization performance, especially when the number of training examples is larger. Moreover, it is less prone to fall into local minima than on-line and batch modes of operation. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing sets. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

Made available in DSpace on 2014-06-11T19:32:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2003-11-28Bitstream added on 2014-06-13T20:03:46Z : No. of bitstreams: 1 cintra_ec_dr_rcla.pdf: 4074998 bytes, checksum: 46f75c3ee3bbcbcc6fcce47c41570c71 (MD5) / Este estudo desenvolve a aplicação da técnica de redes neurais artificiais no controle de teor de minério em frentes de lavra a partir de observações geológicas e geotécnicas. A área de estudo da aplicação é o depósito de cobre e ouro de Chapada (Goiás), hospedado por rochas da seqüência vulcano-sedimentar neoproterozóica de Chapada-Mara Rosa. Trata-se de um depósito mineral tipo epigenético, ligado a processos de alteração hidrotermal, associado a zonas estruturalmente favoráveis. As observações geológicas e geotécnicas constituem um banco de dados com 21.212 registros e 21 variáveis, provenientes de amostras de 237 furos de sondagem rotativa diamantada. As variáveis de entrada incluem litologia, porcentagem de sulfetos, razão calcopirita/pirita, freqüência de fraturas, RQD, e alterações hidrotermais tais como: cloritização, sericitização, silicificação, epidotização, carbonatização e piritização. As variáveis de saída são: teores de cobre e ouro. O modelo de rede neural utilizado foi o de múltiplas camadas (MLP) alimentada adiante ( feedforward ) totalmente interconectada, com 30 neurônios na camada oculta e 2 neurônios na camada de saída. A rede foi treinada com o algoritmo de retropropagação de Levenberg-Marquardt acoplado com regularização bayesiana. Obteve-se um índice de acertos de 80% na predição de teores de cobre em bancadas simuladas. / This study deals with application of artificial neural networks (ANNs) on grade control at mine sites inputting both geological and geotechnical variables. Case study is Chapada copper-gold deposit (Goiás, Brazil), located in the neoproterozoic Chapada-Mara Rosa volcano-sedimentary sequence. Ore is closely related to hydrothermal alteration, structurally controlled. The geological and geotechnical database contain 21,212 records on 21 variables taken from 237 diamond drill holes. Input variables include lithology, sulfide percentage, chalcopyrite/pyrite ratio, fracture frequency, RQD, and hydrothermal alterations such as chloritization, sericitization, silicification, epidotization, carbonatization and pyritization. Output variables are gold and copper grades. Neural network model is feedforward multi-layer perceptron (MLP), fully connected with 30 hidden and 2 output neurons. Network was trained with Levenberg-Marquardt backpropagation algorithm associated with bayesian regularization. Success rate on predicting copper grades on simulated mine benches was over 80%.

abstract: There have been extensive research in how news and twitter feeds can affect the outcome of a given stock. However, a majority of this research has studied the short term effects of sentiment with a given stock price. Within this research, I studied the long-term effects of a given stock price using fundamental analysis techniques. Within this research, I collected both sentiment data and fundamental data for Apple Inc., Microsoft Corp., and Peabody Energy Corp. Using a neural network algorithm, I found that sentiment does have an effect on the annual growth of these companies but the fundamentals are more relevant when determining overall growth. The stocks which show more consistent growth hold more importance on the previous year’s stock price but companies which have less consistency in their growth showed more reliance on the revenue growth and sentiment on the overall company and CEO. I discuss how I collected my research data and used a multi-layered perceptron to predict a threshold growth of a given stock. The threshold used for this particular research was 10%. I then showed the prediction of this threshold using my perceptron and afterwards, perform an f anova test on my choice of features. The results showed the fundamentals being the better predictor of stock information but fundamentals came in a close second in several cases, proving sentiment does hold an effect over long term growth. / Dissertation/Thesis / Masters Thesis Computer Science 2016

Submitted by BARTOLOMEU FÉLIX TANGUNE null (tanguneb@gmail.com) on 2017-05-31T13:12:46Z No. of bitstreams: 1 Bartolomeu Felix Tangune_tese.pdf: 3390592 bytes, checksum: 0daf84bae7e268e5ff6b06e039ea9043 (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-05-31T18:38:01Z (GMT) No. of bitstreams: 1 tangune_bf_dr_bot.pdf: 3390592 bytes, checksum: 0daf84bae7e268e5ff6b06e039ea9043 (MD5) / Made available in DSpace on 2017-05-31T18:38:01Z (GMT). No. of bitstreams: 1 tangune_bf_dr_bot.pdf: 3390592 bytes, checksum: 0daf84bae7e268e5ff6b06e039ea9043 (MD5) Previous issue date: 2017-05-08 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A evapotranspiração de referência (ETo) é importante na agricultura para satisfazer as necessidades de água das culturas e para o manejo dos sistemas de irrigação. A ETo pode ser estimada com precisão a partir do método padrão de Penman Monteith FAO 56, porém, o seu uso é bastante complexo. Sendo assim, vários métodos empíricos de uso simples vem sendo desenvolvidos por diversos pesquisadores, todavia, a sua escolha deve ser feita de forma cuidadosa, pois apresentam um desempenho que varia em função das condições climáticas de cada local. A variabilidade do desempenho dos métodos empíricos tem levado os pesquisadores a procurarem outros métodos alternativos. Como resultado dessas pesquisas, há que destacar a técnica de aprendizado de máquinas (TAM): redes neurais artificiais (RNAs) e máquina vetor de suporte (MVS). Diante do exposto, o presente trabalho foi dividido em três capítulos, onde no primeiro capítulo foi avaliado o desempenho dos métodos empíricos de temperatura (Benevides e Lopez - BenL, Hamon -Ham, Blaney Criddle Original e Hargreaves Samani -HS) e de radiação solar (Abtew, Jensen Haise - JensH, Makkink e Irmak) na estimativa da ETo no estado de São Paulo. Todos os métodos foram avaliados em relação ao método padrão em escala anual e sazonal. Os resultados obtidos na escala anual mostraram que o método de Abtew apresentou o melhor desempenho. Na escala sazonal, observou-se que o método de JensH foi melhor no inverno, o de Irmak e de Abtew no verão e outono. O método de Abtew foi também melhor na primavera. No segundo capítulo, foi avaliado o desempenho dos métodos de HS, e de Abtew (melhores métodos empíricos em escala anual), RNAs e MVS. A RNA utilizada foi do tipo Multilayer Perceptron, com algoritmo de aprendizado Backpropagation e na MVS utilizou-se a função Radial Basic Function de Kernel, com algoritmo Regression Sequential Minimal Optimization. Os resultados obtidos na escala anual mostraram que a R6 (da RNA) e a M6 (da MVS) compostas por temperatura máxima (Tmax), mínima (Tmin), média do ar (T), radiação extraterrestre (Ra) e Rs produziram o melhor desempenho. Na escala sazonal, o melhores resultados foram observados nas arquiteturas R3 e M3, R4 e M4, R5 e M5, R6 e M6, compostas por: Tmax, Tmin, T, Ra e velocidade do vento; Tmax, Tmin, T, Ra e umidade relativa do ar; T e Rs, respectivamente. Tanto no capítulo 1 quanto no 2, as análises estatísticas foram feitas com base nos índices MBE (Mean Bias Error), RSME (Root Mean Square Error), “d” de Willmott e R2 (coeficiente de determinação). No terceiro capítulo, foi avaliada a técnica de interpolação por krigagem ordinária pontual (KOP), cujos variogramas obtidos foram avaliados com base na soma dos quadrados dos resíduos, em escala anual e sazonal. Todos os modelos variográficos obtidos apresentaram uma dependência espacial forte. A posterior, fez-se a validação cruzada da KOP com base nos coeficientes angular e linear da reta de regressão linear simples, MBE, RSME e MSDR (Mean squared deviation ratio ), cujos resultados mostraram um ótimo desempenho da KOP. / The reference evapotranspiration (ETo) is important in agriculture for crop water management and irrigation systems management. The ETo can be estimated accurately by the FAO 56 standard method of Penman Monteith, however, its use is complex. Thus, several empirical methods of simple use have been developed by many researchers, but their choice must be made carefully because they present a performance that change according to the climate conditions of each location. The variability of the performance of empirical methods has led researchers to look for alternative methods. As the result, we must highlight the machine learning technique (MLT), such as artificial neural networks (ANNs) and support vector machine (SVM). This work was divided into three chapters. In the first chapter, four temperature- based (Benevides e Lopez - BenL, Hamon -Ham, Blaney Criddle Original e Hargreaves Samani -HS) and four radiation- based (Abtew, Jensen Haise - JensH, Makkink and Irmak) ETo methods were tested against FAO 56 method, using annual and seasonal scale in the state of São Paulo. The results obtained in the annual scale showed that the Abtew method presented the best performance. On the seasonal scale, it was observed that the JensH method was better in the winter, the Irmak and Abtew methods were better in the summer and autumn. The Abtew method was also better in the spring. In the second chapter, HS and Abtew methods, ANNs and SVM were used. The ANN used was Multilayer Perceptron with Backpropagation learning algorithm, and in the SVM, was used Kernel Radial Basic Function with Regression Sequential Minimal Optimization learning algorithm. The obtained results in the annual scale showed that R6 for RNA and M6 for MVS composed of maximum temperature (Tmax), minimum temperature (Tmin), average air temperature (T), extraterrestrial radiation (Ra) and global solar radiation (Rs) had a better performance. On the seasonal scale, the better performance was observed in R3 e M3, R4 e M4, R5 e M5, R6 e M6 architectures, composed of Tmax, Tmin, T, Ra and wind speed; Tmax, Tmin, T, Ra and relative humidity); T and Rs; R6 and M6, respectively. All methods were analyzed using MBE (Mean Bias Error), RMSE (Root Mean Square Error), “d” of Wilmot (1985) and R2 (determination coefficient). In the third chapter, the technique of interpolation by ordinary punctual kriging (OPK) was evaluated, whose variograms were evaluated based on the residuals sum of squares, on an annual and seasonal scale. All the variographic models obtained showed a strong spatial dependence. Afterwards, cross-validation of OPK was performed based on the angular (β1) and linear (βo) coefficients of the simple linear regression line, MBE, RSME and MSDR (Mean squared deviation ratio), whose results showed an excellent performance of OPK.

Orientador: José Ricardo Sturaro / Banca: Tarcísio Barreto Celestino / Banca: Jorge Kazuo Yamamoto / Banca: Elias Carneiro Daitx / Banca: Paulo Milton Barbosa Landim / Resumo: Este estudo desenvolve a aplicação da técnica de redes neurais artificiais no controle de teor de minério em frentes de lavra a partir de observações geológicas e geotécnicas. A área de estudo da aplicação é o depósito de cobre e ouro de Chapada (Goiás), hospedado por rochas da seqüência vulcano-sedimentar neoproterozóica de Chapada-Mara Rosa. Trata-se de um depósito mineral tipo epigenético, ligado a processos de alteração hidrotermal, associado a zonas estruturalmente favoráveis. As observações geológicas e geotécnicas constituem um banco de dados com 21.212 registros e 21 variáveis, provenientes de amostras de 237 furos de sondagem rotativa diamantada. As variáveis de entrada incluem litologia, porcentagem de sulfetos, razão calcopirita/pirita, freqüência de fraturas, RQD, e alterações hidrotermais tais como: cloritização, sericitização, silicificação, epidotização, carbonatização e piritização. As variáveis de saída são: teores de cobre e ouro. O modelo de rede neural utilizado foi o de múltiplas camadas (MLP) alimentada adiante (“feedforward”) totalmente interconectada, com 30 neurônios na camada oculta e 2 neurônios na camada de saída. A rede foi treinada com o algoritmo de retropropagação de Levenberg-Marquardt acoplado com regularização bayesiana. Obteve-se um índice de acertos de 80% na predição de teores de cobre em bancadas simuladas. / Abstract: This study deals with application of artificial neural networks (ANNs) on grade control at mine sites inputting both geological and geotechnical variables. Case study is Chapada copper-gold deposit (Goiás, Brazil), located in the neoproterozoic Chapada-Mara Rosa volcano-sedimentary sequence. Ore is closely related to hydrothermal alteration, structurally controlled. The geological and geotechnical database contain 21,212 records on 21 variables taken from 237 diamond drill holes. Input variables include lithology, sulfide percentage, chalcopyrite/pyrite ratio, fracture frequency, RQD, and hydrothermal alterations such as chloritization, sericitization, silicification, epidotization, carbonatization and pyritization. Output variables are gold and copper grades. Neural network model is feedforward multi-layer perceptron (MLP), fully connected with 30 hidden and 2 output neurons. Network was trained with Levenberg-Marquardt backpropagation algorithm associated with bayesian regularization. Success rate on predicting copper grades on simulated mine benches was over 80%. / Doutor