Дисертації з теми "Neural Networks method"
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Dunn, Nathan A. "A Novel Neural Network Analysis Method Applied to Biological Neural Networks." Thesis, view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?did=1251892251&sid=2&Fmt=2&clientId=11238&RQT=309&VName=PQD.
Повний текст джерелаTypescript. Includes vita and abstract. Includes bibliographical references (leaves 122- 131). Also available for download via the World Wide Web; free to University of Oregon users.
Chen, Youping. "Neural network approximation for linear fitting method." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1172243968.
Повний текст джерелаCUNHA, JOAO MARCO BRAGA DA. "ESTIMATING ARTIFICIAL NEURAL NETWORKS WITH GENERALIZED METHOD OF MOMENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26922@1.
Повний текст джерелаCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
As Redes Neurais Artificiais (RNAs) começaram a ser desenvolvidas nos anos 1940. Porém, foi a partir dos anos 1980, com a popularização e o aumento de capacidade dos computadores, que as RNAs passaram a ter grande relevância. Também nos anos 1980, houve dois outros acontecimentos acadêmicos relacionados ao presente trabalho: (i) um grande crescimento do interesse de econometristas por modelos não lineares, que culminou nas abordagens econométricas para RNAs, no final desta década; e (ii) a introdução do Método Generalizado dos Momentos (MGM) para estimação de parâmetros, em 1982. Nas abordagens econométricas de RNAs, sempre predominou a estimação por Quasi Máxima Verossimilhança (QMV). Apesar de possuir boas propriedades assintóticas, a QMV é muito suscetível a um problema nas estimações em amostra finita, conhecido como sobreajuste. O presente trabalho estende o estado da arte em abordagens econométricas de RNAs, apresentando uma proposta alternativa à estimação por QMV que preserva as suas boas propriedades assintóticas e é menos suscetível ao sobreajuste. A proposta utiliza a estimação pelo MGM. Como subproduto, a estimação pelo MGM possibilita a utilização do chamado Teste J para verifificar a existência de não linearidade negligenciada. Os estudos de Monte Carlo realizados indicaram que as estimações pelo MGM são mais precisas que as geradas pela QMV em situações com alto ruído, especialmente em pequenas amostras. Este resultado é compatível com a hipótese de que o MGM é menos suscetível ao sobreajuste. Experimentos de previsão de taxas de câmbio reforçaram estes resultados. Um segundo estudo de Monte Carlo apontou boas propriedades em amostra finita para o Teste J aplicado à não linearidade negligenciada, comparado a um teste de referência amplamente conhecido e utilizado. No geral, os resultados apontaram que a estimação pelo MGM é uma alternativa recomendável, em especial no caso de dados com alto nível de ruído.
Artificial Neural Networks (ANN) started being developed in the decade of 1940. However, it was during the 1980 s that the ANNs became relevant, pushed by the popularization and increasing power of computers. Also in the 1980 s, there were two other two other academic events closely related to the present work: (i) a large increase of interest in nonlinear models from econometricians, culminating in the econometric approaches for ANN by the end of that decade; and (ii) the introduction of the Generalized Method of Moments (GMM) for parameter estimation in 1982. In econometric approaches for ANNs, the estimation by Quasi Maximum Likelihood (QML) always prevailed. Despite its good asymptotic properties, QML is very prone to an issue in finite sample estimations, known as overfiting. This thesis expands the state of the art in econometric approaches for ANNs by presenting an alternative to QML estimation that keeps its good asymptotic properties and has reduced leaning to overfiting. The presented approach relies on GMM estimation. As a byproduct, GMM estimation allows the use of the so-called J Test to verify the existence of neglected nonlinearity. The performed Monte Carlo studies indicate that the estimates from GMM are more accurate than those generated by QML in situations with high noise, especially in small samples. This result supports the hypothesis that GMM is susceptible to overfiting. Exchange rate forecasting experiments reinforced these findings. A second Monte Carlo study revealed satisfactory finite sample properties of the J Test applied to the neglected nonlinearity, compared with a reference test widely known and used. Overall, the results indicated that the estimation by GMM is a better alternative, especially for data with high noise level.
Bishop, Russell C. "A Method for Generating Robot Control Systems." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1222394834.
Повний текст джерелаKAIMAL, VINOD GOPALKRISHNA. "A NEURAL METHOD OF COMPUTING OPTICAL FLOW BASED ON GEOMETRIC CONSTRAINTS." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1037632137.
Повний текст джерелаSung, Woong Je. "A neural network construction method for surrogate modeling of physics-based analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43721.
Повний текст джерелаChavali, Krishna Kumar. "Integration of statistical and neural network method for data analysis." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4749.
Повний текст джерелаTitle from document title page. Document formatted into pages; contains viii, 68 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 50-51).
Radhakrishnan, Kapilan. "A non-intrusive method to evaluate perceived voice quality of VoIP networks using random neural networks." Thesis, Glasgow Caledonian University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547414.
Повний текст джерелаMohamed, Ibrahim. "A method for the analysis of the MDTF data using neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0032/MQ62402.pdf.
Повний текст джерелаRowlands, H. "Optimum design using the Taguchi method with neural networks and genetic algorithms." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241701.
Повний текст джерелаStitson, Mark Oliver. "Design, implementation and applications of the Support Vector method and learning algorithm." Thesis, Royal Holloway, University of London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325368.
Повний текст джерелаBittner, Ray Albert. "Development and VLSI implementation of a new neural net generation method." Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-12042009-020129/.
Повний текст джерелаButler, Martin A. "A Method of Structural Health Monitoring for Unpredicted Combinations of Damage." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1575967420002943.
Повний текст джерелаShen, Luou. "Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/17.
Повний текст джерелаGabrié, Marylou. "Towards an understanding of neural networks : mean-field incursions." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE035.
Повний текст джерелаMachine learning algorithms relying on deep new networks recently allowed a great leap forward in artificial intelligence. Despite the popularity of their applications, the efficiency of these algorithms remains largely unexplained from a theoretical point of view. The mathematical descriptions of learning problems involves very large collections of interacting random variables, difficult to handle analytically as well as numerically. This complexity is precisely the object of study of statistical physics. Its mission, originally pointed towards natural systems, is to understand how macroscopic behaviors arise from microscopic laws. In this thesis we propose to take advantage of the recent progress in mean-field methods from statistical physics to derive relevant approximations in this context. We exploit the equivalences and complementarities of message passing algorithms, high-temperature expansions and the replica method. Following this strategy we make practical contributions for the unsupervised learning of Boltzmann machines. We also make theoretical contributions considering the teacher-student paradigm to model supervised learning problems. We develop a framework to characterize the evolution of information during training in these model. Additionally, we propose a research direction to generalize the analysis of Bayesian learning in shallow neural networks to their deep counterparts
Khabou, Mohamed Ali. "Improving shared weight neural networks generalization using regularization theory and entropy maximization /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9953870.
Повний текст джерелаMcFall, Kevin Stanley. "An artificial neural network method for solving boundary value problems with arbitrary irregular boundaries." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-04052006-154934/.
Повний текст джерелаGeorge Vachtsevanos, Committee Member ; Nader Sadegh, Committee Member ; J. Robert Mahan, Committee Chair ; Ali Siadat, Committee Member ; Zhuomin Zhang, Committee Member.
Lout, Kapildev. "Development of a fault location method based on fault induced transients in distribution networks with wind farm connections." Thesis, University of Bath, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.678845.
Повний текст джерелаSequeira, Bernardo Pinto Machado Portugal. "American put option pricing : a comparison between neural networks and least-square Monte Carlo method." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19631.
Повний текст джерелаEsta tese compara dois métodos de pricing de opções de venda Americanas. Os métodos estudados são redes neurais (NN), um método de Machine Learning, e Least-Square Monte Carlo Method (LSM). Em termos de redes neurais foram desenvolvidos dois modelos diferentes, um modelo mais simples, Model 1, e um modelo mais complexo, Model 2. O estudo depende dos preços das opões de 4 gigantes empresas norte-americanas, de Dezembro de 2018 a Março de 2019. Todos os métodos mostram uma precisão elevada, no entanto, uma vez calibradas, as redes neuronais mostram um tempo de execução muito inferior ao LSM. Ambos os modelos de redes neurais têm uma raiz quadrada do erro quadrático médio (RMSE) menor que o LSM para opções de diferentes maturidades e preço de exercício. O Modelo 2 supera substancialmente os outros modelos, tendo um RMSE ca. 40% inferior ao do LSM. O menor RMSE é consistente em todas as empresas, níveis de preço de exercício e maturidade.
This thesis compares two methods to evaluate the price of American put options. The methods are the Least-Square Monte Carlo Method (LSM) and Neural Networks, a machine learning method. Two different models for Neural Networks were developed, a simple one, Model 1, and a more complex model, Model 2. It relies on market option prices on 4 large US companies, from December 2018 to March 2019. All methods show a good accuracy, however, once calibrated, Neural Networks show a much better execution time, than the LSM. Both Neural Network end up with a lower Root Mean Square Error (RMSE) than the LSM for options of different levels of maturity and strike. Model 2 substantially outperforms the other models, having a RMSE ca. 40% lower than that of LSM. The lower RMSE is consistent across all companies, strike levels and maturities.
info:eu-repo/semantics/publishedVersion
Tarullo, Viviana. "Artificial Neural Networks for classification of EMG data in hand myoelectric control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19195/.
Повний текст джерелаMIGDADY, HAZEM MOH'D. "A FEATURES EXTRACTION WRAPPER METHOD FOR NEURAL NETWORKS WITH APPLICATION TO DATA MINING AND MACHINE LEARNING." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/691.
Повний текст джерелаKhoshnoud, Farbod. "A novel modal analysis method based on fuzzy sets." Thesis, Brunel University, 2005. http://bura.brunel.ac.uk/handle/2438/380.
Повний текст джерелаBazargan-Harandi, Hamid. "Neural network based simulation of sea-state sequences." Thesis, Brunel University, 2006. http://bura.brunel.ac.uk/handle/2438/379.
Повний текст джерелаKim, Yong Yook. "Inverse Problems In Structural Damage Identification, Structural Optimization, And Optical Medical Imaging Using Artificial Neural Networks." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11111.
Повний текст джерелаPh. D.
Man, Hou Michael Mechanical & Manufacturing Engineering Faculty of Engineering UNSW. "Implicit coupled constitutive relations and an energy-based method for material modelling." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2009. http://handle.unsw.edu.au/1959.4/43652.
Повний текст джерелаSarlak, Nermin. "Evaluation And Modeling Of Streamflow Data: Entropy Method, Autoregressive Models With Asymmetric Innovations And Artificial Neural Networks." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606135/index.pdf.
Повний текст джерелаAkkala, Arjun. "Development of Artificial Neural Networks Based Interpolation Techniques for the Modeling and Estimation of Radon Concentrations in Ohio." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1279315482.
Повний текст джерелаHluška, Milan. "Využití rychlého algoritmu kosoúhlého rovnání k optimalizaci procesu rovnání pomocí neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-418190.
Повний текст джерелаLiu, Feiyang. "Implementation and verification of the Information Bottleneck interpretation of deep neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235744.
Повний текст джерелаÄ ven om djupa neuronnät (DNN) har gjort anmärkningsvärda framsteg på olikaområden, finns det fortfarande ingen matchande praktisk teori som kan förklara DNNs prestanda. Tishby (2015) föreslog en ny insikt att analysera DNN via informationsflaskhack (IB) -metoden. Genom att visualisera hur mycket relevant information varje lager innehåller i ingång och utgång, hävdade han att DNNs träning består av monteringsfas och kompressionsfas. Monteringsfasenär när DNN lär sig information både i ingång och utgång, och prediktionsnoggrannheten ökar under denna process. Efteråt är det kompressionsfasen när information i utgången bevaras medan orelaterad information i ingången kastas bort. Det här är en kompromiss mellan nätkomplexiteten (komplicerade DNN förlorar mindre information i inmatning) och predictionsnoggrannhet, vilket är exakt samma mål med informationsflaskhals (IB) -metoden.I detta examensarbete kontrollerar vi denna IB-framställning först genom att implementera om Tishby’s arbete, där den dolda lagerfördelningen approximeras av histogrammet (binning). Dessutom introducerar vi olika metoder förömsesidig information uppskattning som kernel density estimators. Baserat på simuleringsresultatet drar vi slutsatsen att det finns en optimal bindning för denömsesidiga informationen mellan dolda lager med ingång och utgång. Men komprimeringen sker huvudsakligen när aktiveringsfunktionen är “dubbelmättad”, som hyperbolisk tangentfunktion.Dessutom utvidgar vi arbetet till den simulerad trådlösa modellen där data set genereras av en trådlös systemsimulator. Resultaten visar att IB-framställning är sann, men binningen är inte ett korrekt verktyg för att approximera dolda lagerfördelningar. Resultatet av denna examensarbete reflekterar informationsvariationerna i varje lager, vilket kan bidra till att välja överföringspa-rameterns konfigurationer i varje ram i trådlösa kommunikationssystem
Flynn, Myles M. 1966. "A method of assessing near-view scenic beauty models: A comparison of neural networks and multiple linear regression." Thesis, The University of Arizona, 1997. http://hdl.handle.net/10150/292054.
Повний текст джерелаBarakat, Mustapha. "Fault diagnostic to adaptive classification schemes based on signal processing and using neural networks." Le Havre, 2011. http://www.theses.fr/2011LEHA0023.
Повний текст джерелаIndustrial Fault Detection and Isolation (FDI) become more essential in light of increased automation in industry. The signifiant increase of systemes and plants complexity during last decades made the FDI tasks appear as major steps in all industrial processes. In this thesis, adaptive intelligent tcehniques based on artificial neural networks combined with advanced signal processing methods for systematic detection and diagnosis of faults in industrial systemes are developed and put forward? The proposed on-line classification techniques consists of three main stages : (1) signal modeling and featured extraction, (2) feature classification and (3) output decision. In first stage, our approach is relied on the assumption that faults are reflected in the extracted features. For feature classification algorithm, several techniques bases on neural networks are proposed. A binary decision tree relied on multiclass Support Vector Machine (SVM) algorithm is put forward. The technique selects dynamic appropriate feature at each level (branch) and classify it in a binary classifier. Another advance classification technique is anticipated based on mapping algorithm network that can extract features from historical data and require prior knowledge about the process. The signifiance of this network focuses on its ability to reserve old data in equitable porpabilities during the mapping process. Each class of faults or disturbances will be represented by a particular surface at the end of the mapping process. Third contribution focuses on building network with nodes that can activate in specific subspaces of different classes. The concept behind this method is to divide the pattern space of faults, in a particular sub-space, a special diagnosis agent is trained. An advanced parameter selection is embedded in this algorithm for improving the confidence of classification diagnosis. All contributions are applied to the fault detection and diagnosis of various industrial systems in the domains of mechanical or chemical engineering. The performances of our approaches are studied and compared with several existing neural network methods and the accuracy of all methodologies is considered carefully and evaluated
Jayasundara, Walpola Kankanamalage Nirmani. "Damage detection of arch bridges using vibration characteristics and artificial neural network." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/135524/1/Walpola%20Kankanamalage%20Nirmani_Jayasundara_Thesis.pdf.
Повний текст джерелаTornstad, Magnus. "Evaluating the Practicality of Using a Kronecker-Factored Approximate Curvature Matrix in Newton's Method for Optimization in Neural Networks." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275741.
Повний текст джерелаAndra ordningens optimeringsmetoder have länge ansetts vara beräkningsmässigt ineffektiva för att lösa optimeringsproblemet inom djup maskininlärning. En alternativ optimiseringsstrategi som använder en Kronecker-faktoriserad approximativ Hessian (KFAC) i Newtons metod för optimering, har föreslagits i tidigare studier. Detta arbete syftar till att utvärdera huruvida metoden är praktisk att använda i djup maskininlärning. Test körs på abstrakta, binära, klassificeringsproblem, samt ett verkligt regressionsproblem: Boston Housing data. Studien fann att KFAC erbjuder stora besparingar i tidskopmlexitet jämfört med när en mer naiv implementation med Gauss-Newton matrisen används. Vidare visade sig losskonvergensen hos både stokastisk gradient descent (SGD) och KFAC beroende av nätverksarkitektur: KFAC tenderade att konvergera snabbare i djupa nätverk, medan SGD tenderade att konvergera snabbare i grunda nätverk. Studien drar slutsatsen att KFAC kan prestera väl för djup maskininlärning jämfört med en grundläggande variant av SGD. KFAC visade sig dock kunna vara mycket känslig för initialvikter. Detta problem kunde lösas genom att låta de första stegen tas av SGD så att KFAC hamnade på en gynnsam bana.
Bhalala, Smita Ashesh 1966. "Modified Newton's method for supervised training of dynamical neural networks for applications in associative memory and nonlinear identification problems." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/277969.
Повний текст джерелаBUENO, ELAINE I. "Group Method of Data Handling (GMDH) e redes neurais na monitoração e detecção de falhas em sensores de centrais nucleares." reponame:Repositório Institucional do IPEN, 2011. http://repositorio.ipen.br:8080/xmlui/handle/123456789/9982.
Повний текст джерелаMade available in DSpace on 2014-10-09T14:05:40Z (GMT). No. of bitstreams: 0
Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP
Shedd, Stephen F. "Semantic and syntactic object correlation in the object-oriented method for interoperability." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FShedd.pdf.
Повний текст джерелаCronley, Thomas J. "The use of neural networks as a method of correlating thermal fluid data to provide useful information on thermal systems." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA380226.
Повний текст джерелаThesis advisor(s): Kelleher, Matthew D. "June 2000." Includes bibliographical references (p. 43). Also available online.
Burger, Christiaan. "A novel method of improving EEG signals for BCI classification." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95984.
Повний текст джерелаENGLISH ABSTRACT: Muscular dystrophy, spinal cord injury, or amyotrophic lateral sclerosis (ALS) are injuries and disorders that disrupts the neuromuscular channels of the human body thus prohibiting the brain from controlling the body. Brain computer interface (BCI) allows individuals to bypass the neuromuscular channels and interact with the environment using the brain. The system relies on the user manipulating his neural activity in order to control an external device. Electroencephalography (EEG) is a cheap, non-invasive, real time acquisition device used in BCI applications to record neural activity. However, noise, known as artifacts, can contaminate the recording, thus distorting the true neural activity. Eye blinks are a common source of artifacts present in EEG recordings. Due to its large amplitude it greatly distorts the EEG data making it difficult to interpret data for BCI applications. This study proposes a new combination of techniques to detect and correct eye blink artifacts to improve the quality of EEG for BCI applications. Independent component analysis (ICA) is used to separate the EEG signals into independent source components. The source component containing eye blink artifacts are corrected by detecting each eye blink within the source component and using a trained wavelet neural network (WNN) to correct only a segment of the source component containing the eye blink artifact. Afterwards, the EEG is reconstructed without distorting or removing the source component. The results show a 91.1% detection rate and a 97.9% correction rate for all detected eye blinks. Furthermore for channels located over the frontal lobe, eye blink artifacts are corrected preserving the neural activity. The novel combination overall reduces EEG information lost, when compared to existing literature, and is a step towards improving EEG pre-processing in order to provide cleaner EEG data for BCI applications.
AFRIKAANSE OPSOMMING: Spierdistrofie, ’n rugmurgbesering, of amiotrofiese laterale sklerose (ALS) is beserings en steurnisse wat die neuromuskulêre kanale van die menslike liggaam ontwrig en dus verhoed dat die brein die liggaam beheer. ’n Breinrekenaarkoppelvlak laat toe dat die neuromuskulêre kanale omlei word en op die omgewing reageer deur die brein. Die BCI-stelsel vertrou op die gebruiker wat sy eie senuwee-aktiwiteit manipuleer om sodoende ’n eksterne toestel te beheer. Elektro-enkefalografie (EEG) is ’n goedkoop, nie-indringende, intydse dataverkrygingstoestel wat gebruik word in BCI toepassings. Nie net senuwee aktiwiteit nie, maar ook geraas , bekend as artefakte word opgeneem, wat dus die ware senuwee aktiwiteit versteur. Oogknip artefakte is een van die algemene artefakte wat teenwoordig is in EEG opnames. Die groot omvang van hierdie artefakte verwring die EEG data wat dit moeilik maak om die data te ontleed vir BCI toepassings. Die studie stel ’n nuwe kombinasie tegnieke voor wat oogknip artefakte waarneem en regstel om sodoende die kwaliteit van ’n EEG vir BCI toepassings te verbeter. Onafhanklike onderdeel analise (Independent component analysis (ICA)) word gebruik om die EEG seine te skei na onafhanklike bron-komponente. Die bronkomponent wat oogknip artefakte bevat word reggestel binne die komponent en gebruik ’n ervare/geoefende golfsenuwee-netwerk om slegs ’n deel van die komponent wat die oogknip artefak bevat reg te stel. Daarna word die EEG hervorm sonder verwringing of om die bron-komponent te verwyder. Die resultate toon ’n 91.1% opsporingskoers en ’n 97.9% regstellingskoers vir alle waarneembare oogknippe. Oogknip artefakte in kanale op die voorste lob word reggestel en behou die senuwee aktiwiteit wat die oorhoofse EEG kwaliteit vir BCI toepassings verhoog.
Upadrasta, Bharat. "Boolean factor analysis a review of a novel method of matrix decomposition and neural network Boolean factor analysis /." Diss., Online access via UMI:, 2009.
Знайти повний текст джерелаIncludes bibliographical references.
Max, Lindblad. "The impact of parsing methods on recurrent neural networks applied to event-based vehicular signal data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223966.
Повний текст джерелаDenna avhandling jämför två olika tillvägagångssätt vad gäller parsningen av händelsebaserad signaldata från fordon för att producera indata till en förutsägelsemodell i form av ett neuronnät, nämligen händelseparsning, där datan förblir ojämnt fördelad över tidsdomänen, och skivparsning, där datan är omgjord till att istället vara jämnt fördelad över tidsdomänen. Det dataset som används för dessa experiment är ett antal signalloggar från fordon som kommer från Scania. Jämförelser mellan parsningsmetoderna gjordes genom att först träna ett lång korttidsminne (LSTM) återkommande neuronnät (RNN) på vardera av de skapade dataseten för att sedan mäta utmatningsfelet och resurskostnader för varje modell efter att de validerats på en delad uppsättning av valideringsdata. Resultaten från dessa tester visar tydligt på att skivparsning står sig väl mot händelseparsning.
Minasny, Budiman. "Efficient Methods for Predicting Soil Hydraulic Properties." University of Sydney. Land, Water & Crop Sciences, 2000. http://hdl.handle.net/2123/853.
Повний текст джерелаMazumdar, Joy. "System and method for determining harmonic contributions from nonlinear loads in power systems." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/23215.
Повний текст джерелаCloyd, James Dale. "Data mining with Newton's method." [Johnson City, Tenn. : East Tennessee State University], 2002. http://etd-submit.etsu.edu/etd/theses/available/etd-1101102-081311/unrestricted/CloydJ111302a.pdf.
Повний текст джерелаPOTIENS, JUNIOR ADEMAR J. "Aplicação de redes neurais artificiais na caracterização isotópica de tambores de rejeito radioativo." reponame:Repositório Institucional do IPEN, 2005. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11354.
Повний текст джерелаMade available in DSpace on 2014-10-09T13:59:15Z (GMT). No. of bitstreams: 1 11135.pdf: 7189578 bytes, checksum: 2301b9d209a5d40ecb7cb637fe73b0f8 (MD5)
Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP
Bueno, Elaine Inacio. "Group Method of Data Handling (GMDH) e redes neurais na monitoração e detecção de falhas em sensores de centrais nucleares." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/85/85133/tde-02092011-140535/.
Повний текст джерелаThe increasing demand in the complexity, efficiency and reliability in modern industrial systems stimulated studies on control theory applied to the development of Monitoring and Fault Detection system. In this work a new Monitoring and Fault Detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANNs) which was applied to the IEA-R1 research reactor at IPEN. The Monitoring and Fault Detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second part to the process information using ANNs. The GMDH algorithm was used in two different ways: firstly, the GMDH algorithm was used to generate a better database estimated, called matrix_z, which was used to train the ANNs. After that, the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one Theoretical Model and four Models using different sets of reactor variables. After an exhausting study dedicated to the sensors Monitoring, the Fault Detection in sensors was developed by simulating faults in the sensors database using values of 5%, 10%, 15% and 20% in these sensors database. The results obtained using GMDH algorithm in the choice of the best input variables to the ANNs were better than that using only ANNs, thus making possible the use of these methods in the implementation of a new Monitoring and Fault Detection methodology applied in sensors.
Torres, Cedillo Sergio Guillermo. "The identification of unbalance in a nonlinear squeeze-film damped system using an inverse method : a computational and experimental study." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/the-identification-of-unbalance-in-a-nonlinear-squeezefilm-damped-system-using-an-inverse-method--a-computational-and-experimental-study(045efa19-bf0f-40de-8657-cb187283a6c6).html.
Повний текст джерелаSebastiani, Andrea. "Deep Plug-and-Play Gradient Method for Super-Resolution." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20619/.
Повний текст джерелаAngelico, João Carlos 1971. "Desempenho das redes neurais artificiais na estimativa das variáveis físicas e químicas do solo /." Botucatu : [s.n.], 2005. http://hdl.handle.net/11449/101867.
Повний текст джерелаAbstract: Statistic methods of interpolation are often used to get the soil characteristics in non-sampled places in order to decrease samples numbers, which are necessary to obtain a good field mapping. In this project, the estimation of soil spatial variability attributes was done in two different ways. First, it was used statistic methods of kriging and cokriging, and in second instance, it was used artificial neural networks. The results computed by both techniques were compared each other in order to verify the efficiency of the artificial neural networks in estimating soil attributes. The results indicated that artificial neural networks, especially Perceptron networks, both with one and two layers of neurons, are able to estimate the soil spatial variability much better than the kriging and the cokriging methods. The artificial neural networks have also showed very efficient in estimating soil variables with respect to its textural class.
Orientador: Ivan Nunes da Silva
Coorientador: José Alfredo Covolan Ulson
Banca: Angelo Cataneo
Banca: Luiz Gonzaga Campos Porto
Banca: Gastão Moraes da Silveira
Banca: Casimiro Dias Gadanha Júnior
Doutor
Marchesan, Gustavo. "Estimadores de frequência aplicados a sistemas elétricos de potência." Universidade Federal de Santa Maria, 2013. http://repositorio.ufsm.br/handle/1/8523.
Повний текст джерелаA estimação de frequência é um problema muito estudado em diversas áreas, dentre elas a dos sistemas elétricos de potência. Inúmeras metodologias foram propostas para esse fim, sendo que a maioria delas apresenta bom desempenho quando o sinal não está distorcido por componentes harmônicas ou ruídos. Este trabalho propõe duas novas metodologias fundamentadas em Redes Neurais Artificiais, de modo a estimar a frequência. Elas utilizam a transformada de Clarck para gerar um fasor que representa o sinal trifásico do sistema. Na primeira metodologia, esse fasor é normalizado e alimenta a Rede Neural de Regressão Generalizada, que faz a ponderação dos valores. Ao final, obtém-se um fasor em que ruídos e harmônicas são atenuados. A saída da rede neural é, então, utilizada para o cálculo da frequência do sistema elétrico. A segunda metodologia utiliza a Rede Neural Linear Adaptativa. Neste trabalho, também são testadas, para uso em sistemas elétricos de potência, diversas metodologias propostas em outras áreas de conhecimento, tais como radar, sonar, comunicação, biomedicina e aviação. São elas: Lavopa (proposta por Lavopa et al. 2007), Quinn (proposta por Quinn, 1994), Jacobsen (proposta por Jacobsen e Kootsookos, 2007), Candan (proposta por Candan, 2011), Macleod (proposta por Macleod, 1998), Aboutanios (proposta por Aboutanios, 2004), Mulgrew (proposta por Aboutanios e Mulgrew, 2005), Ferreira (proposta por Ferreira 2001) e DPLL (proposta por Sithamparanathan, 2008). Com exceção da DPLL, os demais métodos são fundamentados na transformada discreta de Fourier e buscam encontrar o pico do espectro de frequências, para, então, encontrar a frequência fundamental. As nove metodologias são comparadas juntamente com os métodos propostos e as técnicas já comumente usadas ou estudadas para sistemas elétricos. Os testes incluem sinais com ruídos, harmônicas, sub-harmônicas, variações de frequência em degrau, rampa e senoidal, variações de fase e tensão em degrau. Os testes ainda incluem um sinal provindo de simulação em que um bloco de carga é inserido e logo após retirado do sistema. Ao final é realizada uma comparação entre as técnicas, sendo possível identificar as vantagens e desvantagens de cada uma e, assim, indicar as melhores a serem usadas em sistemas elétricos de potência.
Nagaoka, Marilda da Penha Teixeira [UNESP]. "Aplicação de redes neurais em análise de viabilidade econômica de co-geração de energia elétrica." Universidade Estadual Paulista (UNESP), 2005. http://hdl.handle.net/11449/101766.
Повний текст джерелаUniversidade Estadual Paulista (UNESP)
A co-geração de energia elétrica excedente por meio do aproveitamento do bagaço de cana-de-açúcar tem sido considerada uma alternativa importante na diversificação de fontes de geração de energia elétrica no Brasil, considerando-se as vantagens em relação à grande produção de matéria prima, menores custos de geração de energia e a possibilidade de reduzir o ônus dos investimentos em geração de energia do setor público. Apesar do grande potencial apresentado por esta fonte alternativa de energia, o mercado para a energia elétrica co-gerada está ainda hoje, sujeito a um ambiente de grande risco e incerteza, seja decorrente de condições do mercado de energia ou da produção. Este trabalho teve por objetivos analisar a viabilidade econômica de um projeto de investimento em co-geração de energia elétrica em uma usina sucroalcooleira na região Oeste do estado de São Paulo,com vistas à comercialização de excedentes, sob condições de risco, utilizando o algoritmo de Redes Neurais Artificiais. Procurou-se também testar a convergência dos resultados obtidos por este método com outro mais tradicionalmente utilizado em análise de risco para a determinação dos indicadores de viabilidade econômica do investimento. Os indicadores utilizados foram Valor Atual Líquido (VAL); Taxa Interna de Retorno (TIR); Relação Benefício - Custo (RBC); Payback Simples (PBS) e Payback Econômico (PBE). A análise foi realizada considerando seis cenários, considerando a possibilidade ou não de obtenção de financiamento e diferentes níveis de eficiência de queima do bagaço. No método de Redes Neurais Artificiais, as redes foram alimentadas com as seguintes variáveis de entrada: valor do investimento; despesas com juros e amortização; despesa com aquisição e transporte do bagaço e receita operacional, tendo como variável de saída o fluxo líquido de caixa.
The co-generation of surplus electrical energy by means of the use of sugar-cane bagasse has been considered as an important alternative in the diversification of sources of electrical energy in Brazil. Its advantages in relation to the production of raw material are: smaller costs of generation of energy and the possibility to reduce the costs of the investments in the generation of energy in the public sector. In spite of the great potential presented by this alternative source of energy, the market for the co-generation of electrical is still today subject to an atmosphere of great risk and uncertainty, be it due to conditions of the energy or of the production market. The objective of this research study was to analyze the economic viability of an investment project of cogeneration of electrical energy in an alcohol and sugar mill based on the Western area of the state of São Paulo having in view the commercialization of surpluses, under risk conditions, using the algorithm of Artificial Neural Networks. It was also tried to test the convergence of the results obtained by this method with a more traditionally method used in analysis of risk for the determination of the indicators of economic viability of the investment. The indicators used were Liquid Current Value (LCV); Internal Rate of Return (IRR); Benefit - Cost Relationship (BCR); Simple Payback (SPB) and Economic Payback (EPB). The analysis was performed into six different scenarios, having into consideration the possibility or not availabity of financing, and the different levels of efficiency in the burning of bagasse. In the method of Artificial Neural Networks the nets were supplied with entrance variables, such as, the value of the investment; expenses with interests and amortization; expense with acquisition and transport of the bagasse; operational revenue, and the exit variable included the liquid cash flow.