Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 509 for “"artificial neural networks"”.
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Connectome-Constrained Artificial Neural Networks
In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural …
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Porosity distribution prediction using artificial neural networks
… Virginia was selected to conduct this study.;Artificial Neural Networks (ANN) is one of the latest technologies available to the petroleum industry. The objective of this study was to predict reliable porosity values from geophysical log data. In this study, porosity predictions were compared …
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Human iris categorization using artificial neural networks
… a solution of iris image categorization using artificial neural networks, specifically for human iris images with discernible and complicated textures. The work will allow users to quickly and automatically categorize human iris images by using supervised and unsupervised learning algorithms. …
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The evolution of modular artificial neural networks.
… a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming and Genetic Programming; however, these often fail in the evolution of complex …
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Structure of Artificial Neural Networks : Empirical Investigations
… solution methods of countless problems of artificial intelligence. "Deep" refers to the deep architectures with operations in manifolds of which there are no immediate observations. For these deep architectures some kind of structure is pre-defined -- but what is this structure? With a …
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SNP auto-calling using artificial neural networks
In recent years feedforward artificial neural networks (ANN) and their training algorithms have become an effective methodology for the construction of nonlinear systems that solve the statistical problem of classification. The ability of ANNs to solve this problem is highly germane to making …
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OFDM Channel Estimation with Artificial Neural Networks
… This thesis investigates the application of artificial neural networks (ANNs) as a means of improving existing channel estimation techniques. Multi-layer feed forward neural networks (FNNs) and convolutional neural networks (CNNs) are tested on a variety of random fading channels with …
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Iris Biometric Identification Using Artificial Neural Networks
… iris as a personal identifier with the use of neural networks as the classifier. A comparison of different feature extraction methods that include the Fourier transform, discrete cosine transform, the eigen analysis method, and the wavelet transform, is performed. The robustness of each method, …
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Artificial Neural Networks for Programming Quantum Annealers
… It has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some of the fundamental ideas behind quantum machine learning are very similar to kernel methods in classical machine learning. Both process information by mapping …
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Mode Choice Modeling Using Artificial Neural Networks
Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for …
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Modeling Launch Vehicle Success Using Artificial Neural Networks
… into space would decrease. This study used artificial neural networks to model the overall launch outcome of a launch vehicle so that the results of a launch could be predicted. Two neural network architectures--MLP and fuzzy ARTMAP--were trained on historical launch data of Atlas, Delta, …
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Predicting estuarine algal blooms using artificial neural networks
… between 2004 and 2009 has been used to develop Artificial Neural Networks (ANNs) which predict daily mean, 10th and 90th percentile CHLa concentrations. The accuracy of the ANNs to predict CHLa concentrations decreased from one to three to seven days in advance respectively. The ANNs consist of …
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Differentiating noise and modulators in artificial neural networks
Research in Computational Neural Networks is currently taking place at many different levels; from coarse-grain symbolic models to fine-grain representations of neurons and cell processes. One feature that the different approaches share, is that they are all in relative infancy. Thus, most research …
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Using orthogonal arrays to train artificial neural networks.
… the use of Orthogonal Arrays for the training of Artificial Neural Networks. Such arrays are popularly used in system optimisation and are known as Taguchi Methods. The chief advantage of the method is that the network can learn quickly. Fast training methods may be used in certain Control Systems …
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Robust hardware elements for weightless artificial neural networks
… novel robust hardware elements for weightless artificial neural systems with a bias towards high integrity avionics applications. The author initially reviews the building blocks of physiological neural systems and then chronologically describes the development of weightless artificial neural …
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Facility Power Usage Prediction with Artificial Neural Networks
… the global environment. In this research, artificial neural network is employed to model and predict the facility power usage of campus buildings. The prediction is based on the building and the weather conditions such as temperature, humidity, wind speed, etc. Various neural network …
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Bayesian artificial neural networks in water resources engineering.
… for training and selecting the complexity of artificial neural networks (ANNs) is developed in this thesis, based on Markov chain Monte Carlo (MCMC) techniques. The primary motivation of the research presented is the incorporation of uncertainty into ANNs used for water resources modelling, …
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Automated isotope identification algorithm using artificial neural networks
… spectra. Pattern recognition algorithms such as neural networks are prime candidates for automated isotope identification using low-resolution gamma-ray spectra. While algorithms based on feature extraction such as peak finding or ROI algorithms work well for well calibrated high resolution …
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Cost-based shop control using artificial neural networks
… or published approaches. In particular, artificial neural networks and regression nonlinear in its variables are considered. In addition, interactive effects with the third stage, shop-floor dispatching, are taken into consideration. The dissertation conducts three basic studies. The …
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