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Dissertations / Theses on the topic 'Backpropagation Neural Networks'

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Published: 4 June 2025

Last updated: 1 August 2025

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1

Hettinger, Christopher James. "Hyperparameters for Dense Neural Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7531.

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Neural networks can perform an incredible array of complex tasks, but successfully training a network is difficult because it requires us to minimize a function about which we know very little. In practice, developing a good model requires both intuition and a lot of guess-and-check. In this dissertation, we study a type of fully-connected neural network that improves on standard rectifier networks while retaining their useful properties. We then examine this type of network and its loss function from a probabilistic perspective. This analysis leads to a new rule for parameter initialization a

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Bendelac, Shiri. "Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83714.

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Neural networks are making headlines every day as the tool of the future, powering artificial intelligence programs and supporting technologies never seen before. However, the training of neural networks can take days or even weeks for bigger networks, and requires the use of super computers and GPUs in academia and industry in order to achieve state of the art results. This thesis discusses employing selective measures to determine when to backpropagate and forward propagate in order to reduce training time while maintaining classification performance. This thesis tests these new algorithms o

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Bonnell, Jeffrey A. "Implementation of a New Sigmoid Function in Backpropagation Neural Networks." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etd/1342.

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This thesis presents the use of a new sigmoid activation function in backpropagation artificial neural networks (ANNs). ANNs using conventional activation functions may generalize poorly when trained on a set which includes quirky, mislabeled, unbalanced, or otherwise complicated data. This new activation function is an attempt to improve generalization and reduce overtraining on mislabeled or irrelevant data by restricting training when inputs to the hidden neurons are sufficiently small. This activation function includes a flattened, low-training region which grows or shrinks during back-pro

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Civelek, Ferda N. (Ferda Nur). "Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278824/.

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Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems, h

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Yang, Yini. "Training Neural Networks with Evolutionary Algorithms for Flash Call Verification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283039.

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Evolutionary algorithms have achieved great performance among a wide range of optimization problems. In this degree project, the network optimization problem has been reformulated and solved in an evolved way. A feasible evolutionary framework has been designed and implemented to train neural networks in supervised learning scenarios. Under the structure of evolutionary algorithms, a well-defined fitness function is applied to evaluate network parameters, and a carefully derived form of approximate gradients is used for updating parameters. Performance of the framework has been tested by train

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6

Sam, Iat Tong. "Theory of backpropagation type learning of artificial neural networks and its applications." Thesis, University of Macau, 2001. http://umaclib3.umac.mo/record=b1446702.

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7

Chen, Jianhua. "NEURAL NETWORK APPLICATIONS IN AGRICULTURAL ECONOMICS." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_diss/228.

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Neural networks have become very important tools in many areas including economic researches. The objectives of this thesis are to examine the fundamental components, concepts and theory of neural network methods from econometric and statistic perspective, with particular focus on econometrically and statistically relevant models. In order to evaluate the relative effectiveness of econometric and neural network methods, two empirical studies are conducted by applying neural network methods in a methodological comparison fashion with traditional econometric models.Both neural networks and econo

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Batbayar, Batsukh, and S3099885@student rmit edu au. "Improving Time Efficiency of Feedforward Neural Network Learning." RMIT University. Electrical and Computer Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090303.114706.

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Feedforward neural networks have been widely studied and used in many applications in science and engineering. The training of this type of networks is mainly undertaken using the well-known backpropagation based learning algorithms. One major problem with this type of algorithms is the slow training convergence speed, which hinders their applications. In order to improve the training convergence speed of this type of algorithms, many researchers have developed different improvements and enhancements. However, the slow convergence problem has not been fully addressed. This thesis makes seve

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Vieira, Cristiano Ribeiro. "Forecasting financial markets with artificial neural networks." Master's thesis, Instituto Superior de Economia e Gestão, 2013. http://hdl.handle.net/10400.5/6340.

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Mestrado em Matemática Financeira<br>Artificial Neural Networks are exible nonlinear mathematical models widely used in forecasting. This work is intended to investigate the support these models can give to nancial economists predicting prices movements of oil and gas companies listed in stock exchanges. Multilayer Perceptron models with logistic activation functions achieved better results predicting the direction of stocks returns than traditional linear regressions and better performances in companies with lower market capitalization. Furthermore, multilayer perceptron with eight hidden un

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Bastian, Michael R. "Neural Networks and the Natural Gradient." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/539.

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Neural network training algorithms have always suffered from the problem of local minima. The advent of natural gradient algorithms promised to overcome this shortcoming by finding better local minima. However, they require additional training parameters and computational overhead. By using a new formulation for the natural gradient, an algorithm is described that uses less memory and processing time than previous algorithms with comparable performance.

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11

Markham, Ina Samanta. "An exploration of the robustness of traditional regression analysis versus analysis using backpropagation networks." Diss., This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-06062008-170305/.

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12

Staufer-Steinnocher, Petra, and Manfred M. Fischer. "A Neural Network Classifier for Spectral Pattern Recognition. On-Line versus Off-Line Backpropagation Training." WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/4152/1/WSG_DP_6097.pdf.

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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 fo

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Anderson, Thomas. "Built-In Self Training of Hardware-Based Neural Networks." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1512039036199393.

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Draghici, Sorin. "Using constraints to improve generalisation and training of feedforward neural networks : constraint based decomposition and complex backpropagation." Thesis, University of St Andrews, 1996. http://hdl.handle.net/10023/13467.

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Neural networks can be analysed from two points of view: training and generalisation. The training is characterised by a trade-off between the 'goodness' of the training algorithm itself (speed, reliability, guaranteed convergence) and the 'goodness' of the architecture (the difficulty of the problems the network can potentially solve). Good training algorithms are available for simple architectures which cannot solve complicated problems. More complex architectures, which have been shown to be able to solve potentially any problem do not have in general simple and fast algorithms with guarant

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Scarborough, David J. (David James). "An Evaluation of Backpropagation Neural Network Modeling as an Alternative Methodology for Criterion Validation of Employee Selection Testing." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277752/.

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Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biographic predictors using concurrent validation data (N=1085). Accuracy of the neural estimates was compa

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U, San Cho. "Trading simulations on stock market by backpropagation learning of artificial neural networks and traditional linear regression." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1447318.

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Al-Serhan, Hasan Muaidi. "Extraction of Arabic word roots : an approach based on computational model and multi-backpropagation neural networks." Thesis, De Montfort University, 2008. http://hdl.handle.net/2086/4921.

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Stemming is a process of extracting the root of a given word, by stripping off the affixes attached to this word. Many attempts have been made to address the stemming of Arabic words problem. The majority of the existing Arabic stemming algorithms require a complete set of morphological rules and large vocabulary lookup tables. Furthermore, many of them give more than one potential stem or root for a given Arabic word. According to Ahmad [11], the Arabic stemming process based on the language morphological rules is still a very difficult task due to the nature of the language itself. The limit

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18

Jin, Xin. "Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/parallel-simulation-of-neural-networks-on-spinnaker-universal-neuromorphic-hardware(d6b8b72a-63c4-44ee-963a-ae349b0e379c).html.

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Artificial neural networks have shown great potential and have attracted much research interest. One problem faced when simulating such networks is speed. As the number of neurons increases, the time to simulate and train a network increases dramatically. This makes it difficult to simulate and train a large-scale network system without the support of a high-performance computer system. The solution we present is a "real" parallel system - using a parallel machine to simulate neural networks which are intrinsically parallel applications. SpiNNaker is a scalable massively-parallel computing sys

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Keski-Säntti, J. (Jarmo). "Neural networks in the production optimization of a kraft pulp bleach plant." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514285691.

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Abstract Bleaching is an essential process in chemical pulp production for better pulp brightness and longer life expectancy. However, it causes costs such as chemicals, energy, equipment, and loss of yield. Non-linear reactions and several process variables, with interactions, make large plants complicated to model and optimize. As an expensive process bleaching has been a natural target of optimization, but there is still the need to either improve these methods or consider the optimization problem from a new point of view. The aim of this thesis was to develop production optimization meth

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Munasinghe, Aroshine, and Dajana Vlajic. "Stock market prediction using artificial neural networks : A quantitative study on time delays." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168598.

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This report investigates how prediction of stock markets with Artificial Neural Networks (ANN) is affected by altering aspects of data quantities. A short-term and a long-term perspective considering time delays are examined. Inspired by neurosciences, ANNs have shown great potential in terms of recognising patterns in nonlinear systems. Existing research suggests that ANN is an eminent model to predicting stock markets due to its dynamical characteristics. Closing prices of large-caps within the sectors of IT and Telecommunication represented by the Swedish of OMX30 Stockholm (OMXS30), have b

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Trnkóci, Andrej. "Programová knihovna pro práci s umělými neuronovými sítěmi s akcelerací na GPU." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236155.

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Artificial neural networks are demanding to computational power of a computer. Increasing their learning speed could mean new posibilities for research or aplication of the algorithm. And that is a purpose of this thesis. The usage of graphics processing units for neural networks learning is one way how to achieve above mentioned goals. This thesis is offering a survey of theoretical background and consequently implementation of a software library for neural networks learning with a Backpropagation algorithm with a support of acceleration on graphics processing unit.

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Boustany, Ryan. "On deep network training : complexity, robustness of nonsmooth backpropagation, and inertial algorithms." Electronic Thesis or Diss., Université Toulouse Capitole, 2025. http://www.theses.fr/2025TOUC0002.

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L'apprentissage basé sur les réseaux neuronaux repose sur l'utilisation combinée de techniques d'optimisation non convexe de premier ordre, d'approximation par sous-échantillonnage, et de différentiation algorithmique, qui est l'application numérique automatisée du calcul différentiel. Ces méthodes sont fondamentales pour les bibliothèques informatiques modernes telles que TensorFlow, PyTorch et JAX. Cependant, ces bibliothèques utilisent la différentiation algorithmique au-delà de leur cadre primaire sur les opérations différentiables de base. Souvent, les modèles intègrent des fonctions d'ac

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Ayyagari, Suhaas Bhargava. "ARTIFICIAL NEURAL NETWORK BASED FAULT LOCATION FOR TRANSMISSION LINES." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_theses/657.

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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 neura

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Gaspar, Thiago Lombardi. "Reconhecimento de faces humanas usando redes neurais MLP." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-27042006-231620/.

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O objetivo deste trabalho foi desenvolver um algoritmo baseado em redes neurais para o reconhecimento facial. O algoritmo contém dois módulos principais, um módulo para a extração de características e um módulo para o reconhecimento facial, sendo aplicado sobre imagens digitais nas quais a face foi previamente detectada. O método utilizado para a extração de características baseia-se na aplicação de assinaturas horizontais e verticais para localizar os componentes faciais (olhos e nariz) e definir a posição desses componentes. Como entrada foram utilizadas imagens faciais de três bancos distin

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Oliver, Muncharaz Javier. "MODELIZACIÓN DE LA VOLATILIDAD CONDICIONAL EN ÍNDICES BURSÁTILES : COMPARATIVA MODELO EGARCH VERSUS RED NEURONAL BACKPROPAGATION." Doctoral thesis, Editorial Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/35803.

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El siguiente proyecto de tesis pretende mostrar y verificar cómo las redes neuronales, en concreto, la red backpropagation son una alternativa para la predicción de la volatilidad condicional frente a los modelos econométricos clásicos de la familia GARCH. El estudio se realiza para diferentes índices bursátilies de diferentes tamaños y zonas geográficas, así como para datos tanto diarios como de alta frecuencia utilizando para la comparativa uno de los modelos más extendidos para el estudio de la volatildiad condicional en índices bursátiles como el EGARCH, dada la existencia comprobada de as

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Rola, Marcelo Coleto. "Previsão da geração de energia elétrica no médio prazo para o Estado do Rio Grande do Sul empregando redes neurais artificiais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/157828.

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A demanda e, consequentemente, a geração de energia elétrica são questões de suma importância para o desenvolvimento econômico e social dos países. Modelos para previsão destes parâmetros no longo e médio prazo são empregados com a finalidade de antever possíveis cenários e propor estratégias para a realização de um planejamento energético adequado. Neste contexto, o presente estudo tem como objetivo realizar a previsão da geração de energia elétrica no estado do Rio Grande do Sul (RS) em um horizonte de médio prazo (um ano), utilizando Redes Neurais Artificiais (RNA’s) do tipo feedforward com

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Liberman, Felipe. "Classificação de imagens digitais por textura usando redes neurais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1997. http://hdl.handle.net/10183/18578.

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Este trabalho apresenta um estudo sobre a classificação de imagens digitais através da textura com o auxílio de redes neurais. São utilizadas técnicas e conceitos de duas áreas da Informática: O Processamento de Imagens Digitais e a Inteligência Artificial. São apresentados os principais tópicos de processamento de imagens, as principais aplicações em tarefas industriais, reconhecimento de padrões e manipulação de imagens, os tipos de imagem e os formatos de armazenamento. São destacados os atributos da imagem a textura e sua quantificação através da matriz de concorrência dos níveis de cinza.

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Hansson, Jonas. "Image analysis, an approach to measure grass roots from images." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-592.

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<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 colou

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Kane, Andrew. "An instruction systolic array architecture for multiple neural network types." Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/16031.

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Modern electronic systems, especially sensor and imaging systems, are beginning to incorporate their own neural network subsystems. In order for these neural systems to learn in real-time they must be implemented using VLSI technology, with as much of the learning processes incorporated on-chip as is possible. The majority of current VLSI implementations literally implement a series of neural processing cells, which can be connected together in an arbitrary fashion. Many do not perform the entire neural learning process on-chip, instead relying on other external systems to carry out part of th

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Coughlin, Michael J., and n/a. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks." Griffith University. School of Applied Psychology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030409.110949.

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The electro-oculogram (EOG) is the most widely used technique for recording eye movements in clinical settings. It is inexpensive, practical, and non-invasive. Use of EOG is usually restricted to horizontal recordings as vertical EOG contains eyelid artefact (Oster & Stern, 1980) and blinks. The ability to analyse two dimensional (2D) eye movements may provide additional diagnostic information on pathologies, and further insights into the nature of brain functioning. Simultaneous recording of both horizontal and vertical EOG also introduces other difficulties into calibration of the eye moveme

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Coughlin, Michael J. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365854.

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The electro-oculogram (EOG) is the most widely used technique for recording eye movements in clinical settings. It is inexpensive, practical, and non-invasive. Use of EOG is usually restricted to horizontal recordings as vertical EOG contains eyelid artefact (Oster & Stern, 1980) and blinks. The ability to analyse two dimensional (2D) eye movements may provide additional diagnostic information on pathologies, and further insights into the nature of brain functioning. Simultaneous recording of both horizontal and vertical EOG also introduces other difficulties into calibration of the eye moveme

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Olsson, Tim, and Konrad Magnusson. "Training Artificial Neural Networks with Genetic Algorithms for Stock Forecasting : A comparative study between genetic algorithms and the backpropagation of errors algorithms for predicting stock prices." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186447.

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Accurate prediction of future stock market prices is of great importance to traders. The process can be automated using articial neural networks. However, the conventional backward propagation of errors algorithm commonly used for training the networks suffers from the local minima problem. This study investigates whether investing more computational resources into training an ar-ticial neural network using genetic algorithms over the conventional algorithm,to avoid the local minima problem, can result in higher prediction accuracy. The results indicate that there is no signicant increase in a

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Cheng, Martin Chun-Sheng, and pjcheng@ozemail com au. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030722.172812.

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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 inte

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Cheng, Martin Chun-Sheng. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366350.

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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 inte

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Křepský, Jan. "Rekurentní neuronové sítě v počítačovém vidění." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237029.

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The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.

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Ong, Felicia Li Chin. "Heterogeneous Networking for Beyond 3G system in a High-Speed Train Environment. Investigation of handover procedures in a high-speed train environment and adoption of a pattern classification neural-networks approach for handover management." Thesis, University of Bradford, 2016. http://hdl.handle.net/10454/12341.

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Based on the targets outlined by the EU Horizon 2020 (H2020) framework, it is expected that heterogeneous networking will play a crucial role in delivering seamless end-to-end ubiquitous Internet access for users. In due course, the current GSM-Railway (GSM-R) will be deemed unsustainable, as the demand for packet-oriented services continues to increase. Therefore, the opportunity to identify a plausible replacement system conducted in this research study is timely and appropriate. In this research study, a hybrid satellite and terrestrial network for enabling ubiquitous Internet access in a h

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Alvarenga, Rodrigo Jorge. "Reconhecimento de comandos de voz por redes neurais." Universidade de Taubaté, 2012. http://www.bdtd.unitau.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=587.

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Sistema de reconhecimento de fala tem amplo emprego no universo industrial, no aperfeiçoamento de operações e procedimentos humanos e no setor do entretenimento e recreação. O objetivo específico do trabalho foi conceber e desenvolver um sistema de reconhecimento de voz, capaz de identificar comandos de voz, independentemente do locutor. A finalidade precípua do sistema é controlar movimentos de robôs, com aplicações na indústria e no auxílio de deficientes físicos. Utilizou-se a abordagem da tomada de decisão por meio de uma rede neural treinada com as características distintivas do sinal de

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Tangune, Bartolomeu Félix [UNESP]. "Evapotranspiração de referência no estado de São Paulo: métodos empíricos, aprendizado de máquina e geoespacial." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/150790.

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Ollé, Tamás. "Klasifikace vzorů pomocí fuzzy neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219728.

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Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

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Studenikin, Oleg. "Atvirkštinio skleidimo neuroziniai tinklai : vaizdų atpažinimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2005. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2005~D_20050528_172344-92726.

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In this Master’s degree work artificial neural networks and back propagation learning algorithm for human faces and pattern recognition are analyzed. In the second part of work artificial neural networks and their architecture and structures models are analyzed. In the third part of article the backpropagation procedure and procedures theoretical learning principle are analyzed. In the fourth part different kinds of ANN methods and patterns extracting methods in recognition, learning and classification use were researched. In this part RGB method for patterns features extraction was described

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41

He, Shengyang. "Modeling power system load using intelligent methods." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/12036.

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Master of Science<br>Department of Electrical Engineering<br>Shelli K. Starrett<br>Modern power systems are integrated, complex, dynamic systems. Due to the complexity, power system operation and control need to be analyzed using numerical simulation. The load model is one of the least known models among the many components in the power system operation. The two different load models are the static and dynamic models. The ZIP load model has been extensively studied. This has widely applied to composite load models that could maintain constant impedance, constant current, and/or co

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Mendes, Bruno Miguel de Magalhães Soares. "Estudo de incumprimento da situação contributiva e fiscal das empresas utilizando redes neuronais." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/13988.

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Mestrado em Contabilidade, Fiscalidade e Finanças Empresariais<br>O objectivo principal desta dissertação de Mestrado prende-se com o desenvolvimento de um modelo de risco de incumprimento contributivo e fiscal das empresas junto do Estado. Para tal utilizaram-se dados contabilísticos, financeiros e económicos das mesmas. Esta temática, de relevante importância para as empresas, investidores, trabalhadores, Estado e para a consolidação da economia, não tem sido alvo de grande atenção nem por parte dos académicos, nem por parte da sociedade civil. Na construção do modelo recorreu-se a uma téc

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Vicentini, Rafael Estéfano. "Uso de redes neurais artificiais para detecção de pele em imagens digitais." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/152329.

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Submitted by Rafael Estefano Vicentini null (rafaelvicentini@dee.feis.unesp.br) on 2017-12-14T18:01:32Z No. of bitstreams: 1 DISSERTAÇÃO-RAFAEL ESTÉFANO VICENTINI.pdf: 15039479 bytes, checksum: 43a2765c1d39e13b3435f194a64198ec (MD5)<br>Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2017-12-18T10:48:16Z (GMT) No. of bitstreams: 1 vicentini_re_me_ilha.pdf: 15039479 bytes, checksum: 43a2765c1d39e13b3435f194a64198ec (MD5)<br>Made available in DSpace on 2017-12-18T10:48:16Z (GMT). No. of bitstreams: 1 vicentini_re_me_ilha.pdf: 15039479 bytes,

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Kallin, Westin Lena. "Preprocessing perceptrons." Doctoral thesis, Umeå : Univ, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-234.

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Menke, Kurt William. "Nonlinear adaptive control using backpropagating neural networks." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23988.

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46

Hellner, Simon, and Henrik Syvertsson. "Neurala nätverk försjälvkörande fordon : Utforskande av olika tillvägagångssätt." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84560.

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Artificiella neurala nätverk (ANN) har ett brett tillämpningsområde och blir allt relevantare på flera håll, inte minst för självkörande fordon. För att träna nätverken användsmeta-algoritmer. Nätverken kan styra fordonen med hjälp av olika typer av indata. I detta projekt har vi undersökt två meta-algoritmer: genetisk algoritm (GA) och gradient descent tillsammans med bakåtpropagering (GD & BP). Vi har även undersökt två typer av indata: avståndssensorer och linjedetektering. Vi redogör för teorin bakom de metoder vi har försökt implementera. Vi lyckades inte använda GD & BP för att t

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Siddiqui, Muazzam Ahmed. "HIGH PERFORMANCE DATA MINING TECHNIQUES FOR INTRUSION DETECTION." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4435.

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The rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques varying from statistical methods to machine learning algorithms. Intrusion detection systems use audit da

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Khalfaoui, Hassani Ismail. "Convolution dilatée avec espacements apprenables." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES017.

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Dans cette thèse, nous avons développé et étudié la méthode de convolution dilatée avec espacements apprenables (Dilated Convolution with Learnable Spacings en anglais, qu'on abrégera par le sigle DCLS). La méthode DCLS peut être considérée comme une extension de la méthode de convolution dilatée standard, mais dans laquelle les positions des poids d'un réseau de neurones sont apprises grâce à l'algorithme de rétropropagation du gradient, et ce, à l'aide d'une technique d'interpolation. Par suite, nous avons démontré empiriquement l'efficacité de la méthode DCLS en fournissant des preuves conc

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Schilling, Glenn D. "Modeling Aircraft Fuel Consumption with a Neural Network." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36533.

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This research involves the development of an aircraft fuel consumption model to simplify Bela Collins of the MITRE Corporation aircraft fuelburn model in terms of level of computation and level of capability. MATLAB and its accompanying Neural Network Toolbox, has been applied to data from the base model to predict fuel consumption. The approach to the base model and neural network is detailed in this paper. It derives from the basic concepts of energy balance. Multivariate curve fitting techniques used in conjunction with aircraft performance data derive the aircraft specific constants. Aircr

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Fu, Ruijun. "Empirical RF Propagation Modeling of Human Body Motions for Activity Classification." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/1130.

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"Many current and future medical devices are wearable, using the human body as a conduit for wireless communication, which implies that human body serves as a crucial part of the transmission medium in body area networks (BANs). Implantable medical devices such as Pacemaker and Cardiac Defibrillators are designed to provide patients with timely monitoring and treatment. Endoscopy capsules, pH Monitors and blood pressure sensors are used as clinical diagnostic tools to detect physiological abnormalities and replace traditional wired medical devices. Body-mounted sensors need to be investigated

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