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Dissertations / Theses on the topic 'Multi-layer neural networks'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Cairns, Graham Andrew. "Learning with analogue VLSI multi-layer perceptrons." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296901.

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2

Ahmed, Zulfiqar. "An hybrid architecture for multi-layer feed-forward neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0009/MQ52500.pdf.

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3

Tombs, Jonathan Noel. "Multi-layer neural networks and their implementation in analogue VLSI." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334293.

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4

Zheng, Gonghui. "Design and evaluation of a multi-output-layer perceptron." Thesis, University of Ulster, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338195.

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5

Setyawati, Bina R. "Multi-layer feed forward neural networks for foreign exchange time series forecasting." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4180.

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Thesis (Ph. D.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains xii, 185 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 140-146).

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6

Bulbuller, Gokhan. "Recognition of in-ear microphone speech data using multi-layer neural networks." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FBulbuller.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, March 2006.<br>"March 2006." Thesis Advisor(s): Monique P. Fargues, Ravi Vaidyanathan. Includes bibliographical references (p. 159-162). Also available online.

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7

Wang, Hao. "A new scheme for training ReLU-based multi-layer feedforward neural networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217384.

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A new scheme for training Rectified Linear Unit (ReLU) based feedforward neural networks is examined in this thesis. The project starts with the row-by-row updating strategy designed for Single-hidden Layer Feedforward neural Networks (SLFNs). This strategy exploits the properties held by ReLUs and optimizes each row in the input weight matrix individually, under the common optimization scheme. Then the Direct Updating Strategy (DUS), which has two different versions: Vector-Based Method (VBM) and Matrix-Based Method (MBM), is proposed to optimize the input weight matrix as a whole. Finally DU

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8

Penny, William Douglas. "The storage, training and generalization properties of multi-layer logical neural networks." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.331996.

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9

McGarry, Kenneth J. "Rule extraction and knowledge transfer from radial basis function neural networks." Thesis, University of Sunderland, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391744.

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10

Krasniewicz, Jan A. "The application and analysis of genetic algorithms to discover topological free parameters in multi-layer perceptions." Thesis, Birmingham City University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367474.

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11

Golmohammadi, Davood. "A decision making model for evaluating suppliers by multi-layer feed forward neural networks." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5455.

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Thesis (Ph. D.)--West Virginia University, 2007.<br>Title from document title page. Document formatted into pages; contains xiii, 200 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 143-151).

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12

Aung, Min Soe Hane. "Multi layer perception and conic section function neural networks applied to breast cancer risk factors including asymmetry." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400194.

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13

Valmiki, Geetha Charan, and Akhil Santosh Tirupathi. "Performance Analysis Between Combinations of Optimization Algorithms and Activation Functions used in Multi-Layer Perceptron Neural Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20204.

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Background:- Artificial Neural networks are motivated from biological nervous system and can be used for classification and forecasting the data. Each neural node contains activation function could be used for solving non-linear problems and optimization function to minimize the loss and give more accurate results. Neural networks are bustling in the field of machine learning, which inspired this study to analyse the performance variation based on the use of different combinations of the activation functions and optimization algorithms in terms of accuracy results and metrics recall and impact

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14

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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Tran-Canh, Dung. "Simulating the flow of some non-Newtonian fluids with neural-like networks and stochastic processes." University of Southern Queensland, Faculty of Engineering and Surveying, 2004. http://eprints.usq.edu.au/archive/00001518/.

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The thesis reports a contribution to the development of neural-like network- based element-free methods for the numerical simulation of some non-Newtonian fluid flow problems. The numerical approximation of functions and solution of the governing partial differential equations are mainly based on radial basis function networks. The resultant micro-macroscopic approaches do not require any element-based discretisation and only rely on a set of unstructured collocation points and hence are truly meshless or element-free. The development of the present methods begins with the use of the multi-lay

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Ferreira, Ana Paula Carvalho da Silva. "Identificação do funcional da resposta aeroelástica via redes neurais artificiais." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18135/tde-04022016-095107/.

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Identificação e predição do comportamento aeroelástico representa um grande desafio para a análise e controle de fenômenos aeroelásticos adversos. A modelagem aeroelástica requer informações tanto sobre a dinâmica estrutural quanto sobre o comportamento aerodinâmico não estacionário. No entanto, a maioria das metodologias disponíveis atualmente são baseadas no desacoplamento entre o modelo estrutural e o modelo aerodinâmico não estacionário. Conseqüentemente, métodos alternativos são bem vindos na área de pesquisa aerolástica. Entre os métodos alternativos está o funcional multicamada, que for

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Andrade, Kléber de Oliveira. "Sistema neural reativo para o estacionamento paralelo com uma única manobra em veículos de passeio." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-21112011-131734/.

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Graças aos avanços tecnológicos nas áreas da computação, eletrônica embarcada e mecatrônica a robótica está cada vez mais presente no cotidiano da pessoas. Nessas últimas décadas, uma infinidade de ferramentas e métodos foram desenvolvidos no campo da Robótica Móvel. Um exemplo disso são os sistemas inteligentes embarcados nos veículos de passeio. Tais sistemas auxiliam na condução através de sensores que recebem informações do ambiente e algoritmos que analisam os dados e tomam decisões para realizar uma determinada tarefa, como por exemplo estacionar um carro. Este trabalho tem por objetivo

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19

Ak, Ronay. "Neural Network Modeling for Prediction under Uncertainty in Energy System Applications." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0015/document.

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Cette thèse s’intéresse à la problématique de la prédiction dans le cadre du design de systèmes énergétiques et des problèmes d’opération, et en particulier, à l’évaluation de l’adéquation de systèmes de production d’énergie renouvelables. L’objectif général est de développer une approche empirique pour générer des prédictions avec les incertitudes associées. En ce qui concerne cette direction de la recherche, une approche non paramétrique et empirique pour estimer les intervalles de prédiction (PIs) basés sur les réseaux de neurones (NNs) a été développée, quantifiant l’incertitude dans les p

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Bhat, Chandrashekhar. "Artificial Neural Network Approach For Characterization Of Acoustic Emission Sources From Complex Noisy Data." Thesis, Indian Institute of Science, 2001. https://etd.iisc.ac.in/handle/2005/251.

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Safety and reliability are prime concerns in aircraft performance due to the involved costs and risk to lives. Despite the best efforts in design methodology, quality evaluation in production and structural integrity assessment in-service, attainment of one hundred percent safety through development and use of a suitable in-flight health monitoring system is still a farfetched goal. And, evolution of such a system requires, first, identification of an appropriate Technique and next its adoption to meet the challenges posed by newer materials (advanced composites), complex structures and the f

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Bhat, Chandrashekhar. "Artificial Neural Network Approach For Characterization Of Acoustic Emission Sources From Complex Noisy Data." Thesis, Indian Institute of Science, 2001. http://hdl.handle.net/2005/251.

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Safety and reliability are prime concerns in aircraft performance due to the involved costs and risk to lives. Despite the best efforts in design methodology, quality evaluation in production and structural integrity assessment in-service, attainment of one hundred percent safety through development and use of a suitable in-flight health monitoring system is still a farfetched goal. And, evolution of such a system requires, first, identification of an appropriate Technique and next its adoption to meet the challenges posed by newer materials (advanced composites), complex structures and the f

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22

Cherif, Aymen. "Réseaux de neurones, SVM et approches locales pour la prévision de séries temporelles." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4003/document.

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La prévision des séries temporelles est un problème qui est traité depuis de nombreuses années. On y trouve des applications dans différents domaines tels que : la finance, la médecine, le transport, etc. Dans cette thèse, on s’est intéressé aux méthodes issues de l’apprentissage artificiel : les réseaux de neurones et les SVM. On s’est également intéressé à l’intérêt des méta-méthodes pour améliorer les performances des prédicteurs, notamment l’approche locale. Dans une optique de diviser pour régner, les approches locales effectuent le clustering des données avant d’affecter les prédicteurs

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23

Buttar, Sarpreet Singh. "Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87117.

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Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation

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Börthas, Lovisa, and Sjölander Jessica Krange. "Machine Learning Based Prediction and Classification for Uplift Modeling." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266379.

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The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investiga

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Bügner, Jörg. "Nichtlineare Methoden in der trainingswissenschaftlichen Diagnostik : mit Untersuchungen aus dem Schwimmsport." Phd thesis, Universität Potsdam, 2005. http://opus.kobv.de/ubp/volltexte/2005/550/.

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<p>Die trainingswissenschaftliche Diagnostik in den Kernbereichen Training, Wettkampf und Leistungsfähigkeit ist durch einen hohen Praxisbezug, eine ausgeprägte strukturelle Komplexität und vielseitige Wechselwirkungen der sportwissenschaftlichen Teilgebiete geprägt. Diese Eigenschaften haben in der Vergangenheit dazu geführt, dass zentrale Fragestellungen, wie beispielsweise die Maximierung der sportlichen Leistungsfähigkeit, eine ökonomische Trainingsgestaltung, eine effektive Talentauswahl und -sichtung oder die Modellbildung noch nicht vollständig gelöst werden konnten. Neben den bereits v

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Oliveira, Rogério Campos de. "Aplicação de máquinas de comitê de redes neurais artificiais na solução de um problema inverso em transferência radiativa." Universidade do Estado do Rio de Janeiro, 2010. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=1732.

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Este trabalho fundamenta-se no conceito de máquina de comitê de redes neurais artificiais e tem por objetivo resolver o problema inverso de transferência radiativa em um meio unidimensional, homogêneo, absorvedor e espalhador isotrópico. A máquina de comitê de redes neurais artificiais agrega e combina o conhecimento adquirido por um certo número de especialistas aqui representados, individualmente, por cada uma das redes neurais artificiais (RNA) que compõem a máquina de comitê de redes neurais artificiais. O objetivo é atingir um resultado final melhor do que o obtido por qualquer rede neura

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Vaughn, Marilyn Lougher. "Interpretation and knowledge discovery from the multi-layer perceptron neural network." Thesis, Cranfield University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427505.

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Foxall, Robert John. "Likelihood analysis of the multi-layer perceptron and related latent variable models." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327211.

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Sonnert, Adrian. "Predicting inter-frequency measurements in an LTE network using supervised machine learning : a comparative study of learning algorithms and data processing techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148553.

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With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predict

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D'Alimonte, Davide. "Multi layer perceptron neural network algorithms for ocean colour applications in coastal waters." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401830.

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Singh, Arvind. "A multi-layer neural network approach to identification of mechanical damage in power transformer windings." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/5677.

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Power transformers are among the most critical of assets for electric utilities, in the financial impact that their failure can bring. Asset Managers need to be able to determine the right time for replacement, refurbishment or relocation of these devices, with an increasing degree of confidence, in order to minimize the total cost of operation over the equipments’ life. This has brought a change from scheduled maintenance to condition based monitoring (CBM), where the state of the transformer is continuously monitored to evaluate its working condition. A key method of transformer CBM, which e

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Lancashire, Lee James. "Multi-layer perceptron artificial neural network predictive modelling of genomic and mass spectrometry data in bioinformatics." Thesis, Nottingham Trent University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442340.

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Helén, Ludvig. "Automating Text Categorization with Machine Learning : Error Responsibility in a multi-layer hierarchy." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139204.

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The company Ericsson is taking steps towards embracing automating techniques and applying them to their product development cycle. Ericsson wants to apply machine learning techniques to automate the evaluation of a text categorization problem of error reports, or trouble reports (TRs). An excess of 100,000 TRs are handled annually. This thesis presents two possible solutions for solving the routing problems where one technique uses traditional classifiers (Multinomial Naive Bayes and Support Vector Machines) for deciding the route through the company hierarchy where a specific TR belongs. The

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Thorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.

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Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. Making this equipment autonomoustakes many incremental steps, one of which is developing an accurate and reliable tanklevel measurement system. In this thesis, a system for tank level measurement in a seedplanting machine is evaluated. Traditional systems use load cells to measure the weightof the tank however

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Yoon, Moonyoung. "Developing basic soccer skills using reinforcement learning for the RoboCup small size league." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96823.

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Thesis (MSc)--Stellenbosch University, 2015.<br>ENGLISH ABSTRACT: This study has started as part of a research project at Stellenbosch University (SU) that aims at building a team of soccer-playing robots for the RoboCup Small Size League (SSL). In the RoboCup SSL the Decision- Making Module (DMM) plays an important role for it makes all decisions for the robots in the team. This research focuses on the development of some parts of the DMM for the team at SU. A literature study showed that the DMM is typically developed in a hierarchical structure where basic soccer skills form the fund

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Hedström, Erik, and Philip Wang. "Anomaly Detection using a Deep Learning Multi-layer Perceptron to Mitigate the Risk of Rogue Trading." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301948.

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The term Rogue Trading is defined as the activity of someone at a financial organisation losing a large amount of money in bad or illegal transactions and trying to hide this. The activity of Rogue traders exposes financial organisations to huge risks and may lead to the organisation collapsing, which will affect other stakeholders like, for example, the customers. In order to detect potential Rogue Trading cases, Control Systems that monitor the employees and the positions they take on financial markets must exist. In this study, a two-step control system is suggested to monitor the margins o

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FERREIRA, Aida Araújo. "Comparação de arquiteturas de redes neurais para sistemas de reconheceimento de padrões em narizes artificiais." Universidade Federal de Pernambuco, 2004. https://repositorio.ufpe.br/handle/123456789/2465.

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Made available in DSpace on 2014-06-12T15:58:28Z (GMT). No. of bitstreams: 2 arquivo4572_1.pdf: 1149011 bytes, checksum: 92aae8f6f9b5145bfcecb94d96dbbc0b (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2004<br>Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco<br>Um nariz artificial é um sistema modular composto de duas partes principais: um sistema sensor, formado de elementos que detectam odores e um sistema de reconhecimento de padrões que classifica os odores detectados. Redes neurais artificiais têm sido utilizadas

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Lamraoui, Mourad. "Surveillance des centres d'usinage grande vitesse par approche cyclostationnaire et vitesse instantanée." Phd thesis, Université Jean Monnet - Saint-Etienne, 2013. http://tel.archives-ouvertes.fr/tel-01001576.

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Dans l'industrie de fabrication mécanique et notamment pour l'utilisation des centres d'usinage haute vitesse, la connaissance des propriétés dynamiques du système broche-outil-pièce en opération est d'une grande importance. L'accroissement des performances des machines-outils et des outils de coupe a œuvré au développement de ce procédé compétitif. D'innombrables travaux ont été menés pour accroître les performances et les remarquables avancées dans les matériaux, les revêtements des outils coupants et les lubrifiants ont permis d'accroître considérablement les vitesses de coupe tout en améli

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SACHDEVA, NITIN. "CYBERBULLYING DETECTION ON SOCIAL MEDIA USING DEEP LEARNING MODELS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18914.

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Application of deep learning models for cyberbullying detection in social media is an upcoming area for both researchers and practitioners for finding, exploring and analysing the extensibility of human-based expressions. Automated cyberbullying detection is typically a classification problem in natural language processing where the intent is to classify each abusive or offensive comment or post or message or image as either bullying or non-bullying. It needs high-level semantic analysis as well. Most of the earlier attempts on cyberbullying detection rely on manual feature extraction me

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Seselskis, Erikas. ": E-patarėjas galimybėms socialinės atskirties terpėje pasirinkti. Mašinos apsimokymo algoritmų pritaikymas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2006. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20060622_150755-50866.

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At the moment social exclusion is a topical problem in a whole Europe. That’s why innovative decisions are prompted for social exclusive group of people in order to facilitate their integration process into the labour market. The stepping-stone of this work is e-advisor for choosing possibilities within social isolation environment. This e-advisor is created in accordance with artificial neural network and considering to individual person’s features give suggestions for the most suitable professions. Also in this work is presented disease diagnostic model, which is defined by artificial neural

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Guo, Zhihao. "Intelligent multiple objective proactive routing in MANET with predictions on delay, energy, and link lifetime." online version, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1195705509.

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Muñoz, Mas Rafael. "Multivariate approaches in species distribution modelling: Application to native fish species in Mediterranean Rivers." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/76168.

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This dissertation focused in the comprehensive analysis of the capabilities of some non-tested types of Artificial Neural Networks, specifically: the Probabilistic Neural Networks (PNN) and the Multi-Layer Perceptron (MLP) Ensembles. The analysis of the capabilities of these techniques was performed using the native brown trout (Salmo trutta; Linnaeus, 1758), the bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) and the redfin barbel (Barbus haasi; Mertens, 1925) as target species. The analyses focused in the predictive capabilities, the interpretability of the models an

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Silva, Rodrigo Dalvit Carvalho da. "Um estudo sobre a extraÃÃo de caracterÃsticas e a classificaÃÃo de imagens invariantes Ã rotaÃÃo extraÃdas de um sensor industrial 3D." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12154.

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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior<br>Neste trabalho, Ã discutido o problema de reconhecimento de objetos utilizando imagens extraÃdas de um sensor industrial 3D. NÃs nos concentramos em 9 extratores de caracterÃsticas, dos quais 7 sÃo baseados nos momentos invariantes (Hu, Zernike, Legendre, Fourier-Mellin, Tchebichef, Bessel-Fourier e Gaussian-Hermite), um outro Ã baseado na Transformada de Hough e o Ãltimo na anÃlise de componentes independentes, e, 4 classificadores, Naive Bayes, k-Vizinhos mais PrÃximos, MÃquina de Vetor de Suporte e Rede Neural Artificial-Perceptr

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Correia, Mauro Vicentini. "Redes neurais e algoritmos genéticos no estudo quimiossistemático da família Asteraceae." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/46/46135/tde-12082013-153222/.

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No presente trabalho duas metodologias da área de inteligência artificial (Redes Neurais e Algoritmos Genéticos) foram utilizadas para realizar um estudo Quimiossistemático da família Asteraceae. A família Asteraceae é uma das maiores famílias entre as Angiospermas, conta com aproximadamente 24.000 espécies. As espécies da família produzem grande diversidade de metabólitos secundários, entre os quais merecem destaque os terpenóides, poliacetilenos, flavonóides e cumarinas. Para um melhor entendimento da diversidade química da família construiu-se um Banco de Dados com as ocorrências de doze cl

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Zdybek, Mia. "Evaluating deep learning models for electricity spot price forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302642.

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Electricity spot prices are difficult to predict since they depend on different unstable and erratic parameters, and also due to the fact that electricity is a commodity that cannot be stored efficiently. This results in a volatile, highly fluctuating behavior of the prices, with many peaks. Machine learning algorithms have outperformed traditional methods in various areas due to their ability to learn complex patterns. In the last decade, deep learning approaches have been introduced in electricity spot price prediction problems, often exceeding their predecessors. In this thesis, several dee

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SARCIA', SALVATORE ALESSANDRO. "An Approach to improving parametric estimation models in the case of violation of assumptions based upon risk analysis." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2009. http://hdl.handle.net/2108/1048.

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In this work, we show the mathematical reasons why parametric models fall short of providing correct estimates and define an approach that overcomes the causes of these shortfalls. The approach aims at improving parametric estimation models when any regression model assumption is violated for the data being analyzed. Violations can be that, the errors are x-correlated, the model is not linear, the sample is heteroscedastic, or the error probability distribution is not Gaussian. If data violates the regression assumptions and we do not deal with the consequences of these violations, we cannot i

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Hoskins, Bradley Graham. "The design and application of multi-layer neural networks." 1995. http://arrow.unisa.edu.au:8081/1959.8/84000.

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Muller, Salomon. "Learning in Multi-Layer Networks of the Brain." Thesis, 2021. https://doi.org/10.7916/d8-wh1n-c795.

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Simple circuits perform simple tasks. Complex circuits can perform more complicated tasks. This is true for artificial circuits and for brain circuits. As is known from artificial networks, a complexity that makes circuits substantially more powerful is distributing learning across multiple layers. In fact, most brain circuits in vertebrate systems are multi-layer circuits (but for few that perform simple reflexes) in which learning is distributed across layers. Despite the crucial contribution of learning in middle layer neurons to the output of the circuits they are embedded in, there is lit

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Yu, Wen-Jen, and 于文貞. "The Study of the Learning Ability of Multi-layer Neural Networks." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/62408512533758493389.

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碩士<br>國立臺灣大學<br>資訊工程研究所<br>82<br>The objective of this research is to propose methods on solving the learning problems of multi-layer neural networks. The most well-known and commonly used learning algorithm is Back Propagation (BP) algorithm. There are three main drawbacks of BP: 1. the slowness of the learning speed, 2. the convergence to local minima, and 3. the absence of any theoretical result, allowing for a priori determination of an optimal network architecture for a given task. To

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HOU-TE, CHIANG, and 江厚德. "Investigations and Implementations for Recur-rent Neural Networks and Feedforward Multi-ple-Layer Perceptron." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8up2fe.

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碩士<br>亞洲大學<br>資訊工程學系碩士在職專班<br>106<br>In this thesis, a multiple layer perceptron (MLP) and a recurrent neural network (RNN) are proposed for investigation and comparison in the sense of learning performances. The proposed RNN is a local feedback network and is composed by a state space realization. By using back propagation learning algorithm, the learning performances of the proposed RNN is better than that of the conven-tional MLP. Finally, numerical examples are performed to illustrate the effec-tiveness of the proposed approach.

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