Se analiza el uso de redes neuro-difusas para solucionar problemas de clasificación y modelización. El objetivo es intentar combinar las cualidades de las redes neuronales y de la descripción de sistemas mediante Lógica Difusa. Las redes neuronales son conocidas por su alta capacidad de aprendizaje, lo que permite una adecuada generalización en el tipo de problemas comentado anteriormente. Su aplicación a problemas reales no ha dejado de crecer durante los últimos años. Por otro lado, la Lógica Difusa es una herramienta más novedosa, cuya propiedad más atractiva es la capacidad que posee de poder tratar con variables numéricas y variables lingüísticas simultáneamente. Las variables lingüísticas permiten un tratamiento del problema más comprensible y cercano al cono...

This dissertation will discuss the uncertainty encountered in the daily operations of businesses. The concepts will be developed by first giving an overview of probability and statistics as used in our everyday activities, such as the basic principles of probability, univariate and multivariate statistics, data clustering and mapping, as well as time sequence and spectral analysis. The examples used will be from the oil and gas exploration industry because the risks taken in this industry are normally quite large and are ideal for showing the application of the various techniques for minimizing risk. Subsequently, the discussion will deal with basic risk analysis, spatial and time variations of risk, geotechnical risk analysis, risk aversion and how it is affected by personal biases, an...

Weaving Dreams into the Classroom is an extraordinary anthology which combines the seasoned experience of ten educators at all educational levels to provide the reader with practical, hands-on models for bringing the subject of dreams and dreaming to students. It also includes the perspective of a teenage student who has been embedded in a dream-centered education program since early childhood. The authors come from diverse backgrounds, including academic and clinical psychology, anthropology, and religious studies. Their home institutions range from small private colleges and institutes to large research universities, both in the United States and Great Britain. PRAISE FOR WEAVING DREAMS INTO THE CLASSROOM In recent years, there has been an unprecedented interest in dreams and how t...

This thesis is dedicated to assess the accuracy of logistic regression (LR) and artificial neural networks (ANN) in the diagnosis of calvarial lesions using computed tomography (CT). The importance of the different features needed for the diagnosis in both models is also analyzed. The models were developed using patients with calvarial lesions as the only known disease were enrolled. All patients were studied with plain films and CT. Other imaging thecniques were used when available. The clinical and CT data were used for developing LR and ANN models. Both models were tested with the jacknife (leave-one-out) method. The best ANNs were obtained varying iterations and hidden neurons by selecting the one with higher area under the receiver operating characteristic curve (ROC). The fi...

Data assimilation of satellite-based observations of hydrological variables with full numerical physics models can be used to downscale these observations from coarse to high resolution to improve microwave sensor-based soil moisture observations. Moreover, assimilation can also be used to predict related hydrological variables, e.g., precipitation products can be assimilated in a land information system to estimate soil moisture. High quality spatio-temporal observations of these processes are vital for a successful assimilation which in turn needs a detailed analysis and improvement. In this research, pattern recognition and adaptive signal processing methods are developed for the spatio-temporal analysis and enhancement of soil moisture and precipitation datasets. These methods are app...

Upper body power (UBP) is one of the most important factors affecting the performance of cross-country skiers during races. Although some initial studies have already attempted to predict UBP, until now, no study has attempted to apply machine learning methods combined with various feature selection algorithms to identify the discriminative features for prediction of UBP. The purpose of this study is to develop new prediction models for predicting the 10-second UBP (UBP10) and 60-second UBP (UBP60) of cross-country skiers by using General Regression Neural Networks (GRNN), Radial-Basis Function Network (RBF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Single Decision Tree (SDT) and Tree Boost (TB) along with the Relief-F feature selection algorithm, minimum redundancy maxim...