Tesis sobre el tema "Multi-layer perceptrons"
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Zhao, Lenny. "Uncertainty prediction with multi-layer perceptrons". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0018/MQ55733.pdf.
Texto completoCairns, 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.
Texto completoPapadopoulos, Georgios. "Theoretical issues and practical considerations concerning confidence measures for multi-layer perceptrons". Thesis, University of Edinburgh, 2000. http://hdl.handle.net/1842/12753.
Texto completoShepherd, Adrian John. "Novel second-order techniques and global optimisation methods for supervised training of multi-layer perceptrons". Thesis, University College London (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321662.
Texto completoCollobert, Ronan. "Algorithmes d'Apprentissage pour grandes bases de données". Paris 6, 2004. http://www.theses.fr/2004PA066063.
Texto completoShao, Hang. "A Fast MLP-based Learning Method and its Application to Mine Countermeasure Missions". Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23512.
Texto completoCoughlin, Michael J. y 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.
Texto completoCoughlin, Michael J. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365854.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
Griffith Health
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Dunne, R. A. "Multi-layer perceptron models for classification". Thesis, Dunne, R.A. (2003) Multi-layer perceptron models for classification. PhD thesis, Murdoch University, 2003. https://researchrepository.murdoch.edu.au/id/eprint/50257/.
Texto completoPower, Phillip David. "Non-linear multi-layer perceptron channel equalisation". Thesis, Queen's University Belfast, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343086.
Texto completoCherif, Aymen. "Réseaux de neurones, SVM et approches locales pour la prévision de séries temporelles". Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4003/document.
Texto completoTime series forecasting is a widely discussed issue for many years. Researchers from various disciplines have addressed it in several application areas : finance, medical, transportation, etc. In this thesis, we focused on machine learning methods : neural networks and SVM. We have also been interested in the meta-methods to push up the predictor performances, and more specifically the local models. In a divide and conquer strategy, the local models perform a clustering over the data sets before different predictors are affected into each obtained subset. We present in this thesis a new algorithm for recurrent neural networks to use them as local predictors. We also propose two novel clustering techniques suitable for local models. The first is based on Kohonen maps, and the second is based on binary trees
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.
Texto completoLont, Jerzy B. "Analog CMOS implementation of a multi-layer perceptron with nonlinear synapses /". [S.l.] : [s.n.], 1993. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=10244.
Texto completoVaughn, 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.
Texto completoSiu, Sammy. "Non-linear adaptive equalization based on a multi-layer perceptron architecture". Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/11916.
Texto completoFoxall, 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.
Texto completoD'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.
Texto completoKybartas, Rimantas. "Multi-class recognition using pair-wise classifiers". Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20101001_150424-92661.
Texto completoDaugelio klasių atpažinimo uždaviniams spręsti yra sukurta aibė sprendimų ir ne visada vieningų rekomendacijų. Dauguma jų paremta empiriniais bandymais, retai atsižvelgiama į statistines duomenų savybes. Dėl to sprendžiant daugelio klasių klasifikavimo uždavinį kyla klausimų, kurį metodą ir kada geriausia naudoti, koks vieno ar kito metodo patikimumas. Disertacijoje nagrinėjami dviejų pakopų sprendimo priėmimo metodai, kai pirmame etape sudaromi klasifikatoriai poroms (angl. pair-wise), sugebantys geriau išnaudoti klasių tarpusavio statistines savybes, o kitame etape yra atliekamas klasifikatorių poroms rezultatų apjungimas. Tyrime ypatingas dėmesys yra skiriamas klasifikatorių poroms sudėtingumui, mokymo duomenų kiekiui bei algoritmų kokybės įvertinimo tikslumui. Tikslumas labai priklauso nuo duomenų bei atliktų eksperimentų kiekio (duomenų permaišymo klasėse, juos skirstant į mokymo ir testavimo). Parodyta, jog dėl žemo įvertinimo tikslumo kai kurių publikuotų algoritmų deklaruojamas pranašumas prieš žinomus algoritmus nėra patikimas. Darbe atliktas detalus žinomų metodų palyginimas bei pristatytas naujai sukurtas klasifikatorių poroms apjungimo algoritmas, kuris yra paremtas analogišku algoritmu daugelio klasių klasifikatorių rezultatų apjungimui. Pateiktos bendros rekomendacijos, kaip projektuotojui elgtis daugelio klasių atveju. Pasiūlyti metodai, leidžiantys sumažinti klasifikavimo klaidą atliekant klasifikatorių poroms apjungimo koregavimą, kad algoritmas nebūtų... [toliau žr. visą tekstą]
Kybartas, Rimantas. "Daugelio klasių atpažinimas naudojant klasifikatorius poroms". Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20101001_150435-26873.
Texto completoThere are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the second stage a particular fusion rule of the first stage results is used to fuse the first stage results in order to produce the final classification decision. Complexity issues of pair-wise classifiers, training data size and precision of method quality estimation are pointed out in the research. The precision of algorithm highly depends on the data and the number of experiments performed (data permutation, division into training and testing data). It is shown that the declared superiority of some known algorithms is not reliable due to low precision of estimation. A detailed comparison of well known multi-class classification methods is performed and a new pair-wise classifier fusion method based on similar method used in multi-class classifier fusion is presented. The recommendations for multi-class classification task designer are provided. Methods which allow reducing classification... [to full text]
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.
Texto completoVural, Hulya. "Comparison Of Rough Multi Layer Perceptron And Rough Radial Basis Function Networks Using Fuzzy Attributes". Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605293/index.pdf.
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, &ldquo
medium&rdquo
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. In the rough fuzzy MLP, initial weights and near optimal number of hidden nodes are estimated using rough dependency rules. A rough fuzzy RBF structure similar to the rough fuzzy MLP is proposed. The rough fuzzy RBF was inspected whether dependencies like the ones in rough fuzzy MLP can be concluded.
Evans, John Thomas. "Investigation of a multi-layer perceptron network to model and control a non-linear system". Thesis, Liverpool John Moores University, 1994. http://researchonline.ljmu.ac.uk/4945/.
Texto completoDlugosz, Stephan. "Multi-layer perceptron networks for ordinal data analysis : order independent online learning by sequential estimation /". Berlin : Logos, 2008. http://d-nb.info/990567311/04.
Texto completoLancashire, 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.
Texto completoHedström, Erik y 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.
Texto completoTermen Rouge Trading definieras som en aktivitet där någon på en finansiell institution förlorar stora mängder pengar i dåliga eller illegala transaktioner och försöker dölja detta. Detta är något som skapar enorma risker för finansiella institutioner och som kan förorsaka organisationens kollaps, som kan påverka intressenter som till exempel kunder. För att upptäcka potentiella företeelser av Rouge Trading så måste kontrollsystem som övervakar anställda och deras positioner existera. I denna studie föreslås och presenteras ett tvåstegs-system för att övervaka marginaler vid terminsaffärer i utländsk valuta vid Skandinaviska Enskilda Banken (SEB). Det första steget i kontrollsystemet använder ett neuralt närverk tränat på data från transaktioner för att prediktera en marginal. Differenserna mellan prediktionen och det faktiska värdet används för att finna outliers vilka borde flaggas för vidare undersökning. Resultaten visar att modellen förhoppningsvis kan minska antalet falska positiva som det nuvarande kontrollsystemet ger på SEB, något som således kan minska den manuella inspektionen av flaggade transaktioner.
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.
Texto completoValmiki, Geetha Charan y 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.
Texto completoHåkansson, Ellinor. "A Deep Learning Approach to Predicting Diagnosis Code from Electronic Health Records". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240599.
Texto completoElektronisk patientjournal (EHR) är ett paraplybegrepp som används för att beskriva en digital samling av demografisk och medicinsk data från olika källor för en patient. Det finns stor potential i användandet av djupinlärning på dessa journaler och många framgångsrika studier har redan gjorts på området. I denna studie undersöks diagnosklassificering av elektroniska patientjournaler med Multi-layer perceptronmodeller. Två MLP-modeller av olika arkitekturer presenteras. Dessa körs på både en anpassad version av EHR-datamängden och på den råa EHR-datan. En Random Forest-modell används som baslinje för jämförelse. MLP-modellerna lyckas inte överträffa baslinjen, då den bästa MLP-modellen ger en klassifikationsnoggrannhet på 48,1%, vilket är 13,7 procentenheter mindre än baslinjens. Resultaten visar att en liten datamängd indikerar att djupinlärning bör väljas bort för denna typ av problem. Datamängden växer dock över tid, vilket gör områdetattraktivt för framtida studier.
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.
Texto completoENGLISH 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 fundamental building blocks and high-level team behaviours are implemented using these basic soccer skills. The literature study also revealed that strategies in the DMM are usually developed using a hand-coded approach in the RoboCup SSL domain, i.e., a specific and fixed strategy is coded, while in other leagues a Machine Learning (ML) approach, Reinforcement Learning (RL) in particular, is widely used. This led to the following research objective of this thesis, namely to develop basic soccer skills using RL for the RoboCup Small Size League. A second objective of this research is to develop a simulation environment to facilitate the development of the DMM. A high-level simulator was developed and validated as a result. The temporal-difference value iteration algorithm with state-value functions was used for RL, along with a Multi-Layer Perceptron (MLP) as a function approximator. Two types of important soccer skills, namely shooting skills and passing skills were developed using the RL and MLP combination. Nine experiments were conducted to develop and evaluate these skills in various playing situations. The results showed that the learning was very effective, as the learning agent executed the shooting and passing tasks satisfactorily, and further refinement is thus possible. In conclusion, RL combined with MLP was successfully applied in this research to develop two important basic soccer skills for robots in the RoboCup SSL. These form a solid foundation for the development of a complete DMM along with the simulation environment established in this research.
AFRIKAANSE OPSOMMING: Hierdie studie het ontstaan as deel van 'n navorsingsprojek by Stellenbosch Universiteit wat daarop gemik was om 'n span sokkerrobotte vir die RoboCup Small Size League (SSL) te ontwikkel. Die besluitnemingsmodule (BM) speel 'n belangrike rol in die RoboCup SSL, aangesien dit besluite vir die robotte in die span maak. Hierdie navorsing fokus op ontwikkeling van enkele komponente van die BM vir die span by SU. 'n Literatuurstudie het getoon dat die BM tipies ontwikkel word volgens 'n hiërargiese struktuur waarin basiese sokkervaardighede die fundamentele boublokke vorm en hoëvlak spangedrag word dan gerealiseer deur hierdie basiese vaardighede te gebruik. Die literatuur het ook getoon dat strategieë in die BM van die RoboCup SSL domein gewoonlik ontwikkel word deur 'n hand-gekodeerde benadering, dit wil s^e, 'n baie spesifieke en vaste strategie word gekodeer, terwyl masjienleer (ML) en versterkingsleer (VL) wyd in ander ligas gebruik word. Dit het gelei tot die navorsingsdoelwit in hierdie tesis, naamlik om basiese sokkervaardighede vir robotte in die RoboCup SSL te ontwikkel. 'n Tweede doelwit was om 'n simulasie-omgewing te ontwikkel wat weer die ontwikkeling van die BM sou fasiliteer. Hierdie simulator is suksesvol ontwikkel en gevalideer. Die tydwaarde-verskil iterariewe algoritme met toestandwaarde-funksies is gebruik vir VL saam met 'n multi-laag perseptron (MLP) vir funksiebenaderings. Twee belangrike sokkervaardighede, naamlik doelskop- en aangeevaardighede is met hierdie kombinasie van VL en MLP ontwikkel. Nege eksperimente is uitgevoer om hierdie vaardighede in verskillende speelsituasies te ontwikkel en te evalueer. Volgens die resultate was die leerproses baie effektief, aangesien die leer-agent die doelskiet- en aangeetake bevredigend uitgevoer het, en verdere verfyning is dus moontlik. Die gevolgtrekking is dat VL gekombineer met MLP suksesvol toegepas is in hierdie navorsingswerk om twee belangrike, basiese sokkervaardighede vir robotte in die RoboCup SSL te ontwikkel. Dit vorm 'n sterk fondament vir die ontwikkeling van 'n volledige BM tesame met die simulasie-omgewing wat in hierdie werk daargestel is.
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/.
Texto completoThanks to technological advances in the fields of computer science, embedded electronics and mechatronics, robotics is increasingly more present in people\'s lives. On the past few decades a great variety of tools and methods were developed in the Mobile Robotics field, e.g. the passenger vehicles with smart embedded systems. Such systems help drivers through sensors that acquire information from the surrounding environment and algorithms which process this data and make decisions to perform a task, like parking a car. This work aims to present the studies performed on the development of a smart controller able to park a simulated vehicle in parallel parking spaces, where a single maneuver is enough to enter. To accomplish this, studies involving the modeling of environments, vehicle kinematics and sensors were conducted, which were implemented in a simulated environment developed in C# with Visual Studio 2008. Next, a study about the three stages of parking was carried out, which consists in looking for a slot, positioning the vehicle and maneuvering it. The \"S\" trajectory was adopted and developed to maneuver the vehicle, since it is well known and highly used in related works found in the literature of this field. The maneuver consists in the correct positioning of two circumferences with the possible steering radius of the vehicle. For this task, a robust controller based on supervised learning using Artificial Neural Networks (ANN) was employed, since this approach has great robustness regarding the presence of noise in the system. This controller receives data from two laser sensors (one attached on the front of the vehicle and the other on the rear), from the odometry and from the inertial orientation sensor. The data acquired from these sensors and the current maneuver stage of the vehicle are the inputs of the controller, which interprets these data and responds to these stimuli in a correct way in approximately 99% of the cases. The results of the training and simulation were satisfactory, allowing the car controlled by the ANN to correctly park in a parallel slot.
ITAKURA, Fumitada, Kazuya TAKEDA, Katsunobu ITOU y Weifeng LI. "Single-Channel Multiple Regression for In-Car Speech Enhancement". Institute of Electronics, Information and Communication Engineers, 2006. http://hdl.handle.net/2237/15051.
Texto completoCerna, Ñahuis Selene Leya. "Comparative analysis of XGBoost, MLP and LSTM techniques for the problem of predicting fire brigade Iiterventions /". Ilha Solteira, 2019. http://hdl.handle.net/11449/190740.
Texto completoAbstract: Many environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and, as a result, they face an ever-increasing number of interventions, most of the time on constant resource. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters on constant resource is therefore to predict their workload, i.e., their number of interventions per hour, based on explanatory variables conditioning human activity. The present work aims to develop three models that are compared to determine if they can predict the firefighters' response load in a reasonable way. The tools chosen are the most representative from their respective categories in Machine Learning, such as XGBoost having as core a decision tree, a classic method such as Multi-Layer Perceptron and a more advanced algorithm like Long Short-Term Memory both with neurons as a base. The entire process is detailed, from data collection to obtaining the predictions. The results obtained prove a reasonable quality prediction that can be improved by data science techniques such as feature selection and adjustment of hyperparameters.
Resumo: Muitos fatores ambientais, econômicos e sociais estão levando as brigadas de incêndio a serem cada vez mais solicitadas e, como consequência, enfrentam um número cada vez maior de intervenções, na maioria das vezes com recursos constantes. Por outro lado, essas intervenções estão diretamente relacionadas à atividade humana, o que é previsível: os afogamentos em piscina ocorrem no verão, enquanto os acidentes de tráfego, devido a tempestades de gelo, ocorrem no inverno. Uma solução para melhorar a resposta dos bombeiros com recursos constantes é prever sua carga de trabalho, isto é, seu número de intervenções por hora, com base em variáveis explicativas que condicionam a atividade humana. O presente trabalho visa desenvolver três modelos que são comparados para determinar se eles podem prever a carga de respostas dos bombeiros de uma maneira razoável. As ferramentas escolhidas são as mais representativas de suas respectivas categorias em Machine Learning, como o XGBoost que tem como núcleo uma árvore de decisão, um método clássico como o Multi-Layer Perceptron e um algoritmo mais avançado como Long Short-Term Memory ambos com neurônios como base. Todo o processo é detalhado, desde a coleta de dados até a obtenção de previsões. Os resultados obtidos demonstram uma previsão de qualidade razoável que pode ser melhorada por técnicas de ciência de dados, como seleção de características e ajuste de hiperparâmetros.
Mestre
Santos, Davi Pereira dos. "Seleção de características: abordagem via redes neurais aplicada à segmentação de imagens". Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-08052007-145719/.
Texto completoSegmentation is a crucial step in Computer Vision. Texture has been a property largely employed by many researchers to achieve segmentation. The existence of a large amount of texture extraction methods is, sometimes, a hurdle to overcome when it comes to modeling systems for more general problems. Inside this context and following the excellence of natural vision systems and their generality, this work has adopted a feature selection method based on synaptic conexions salience of a Multilayer Perceptron and a method based on its texture inference capability. As well as comparing the proposed method with exhaustive search according to the Jeffrey-Matusita distance criterion, this work also introduces, as a major contribution, the Input Equalization technique, which contributed to significantly improve the segmentation results. The segmentation of images of natural scenes has also been provided as a likely application of the method
Senlet, Turgay. "Vision Based Obstacle Detection And Avoidance Using Low Level Image Features". Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607229/index.pdf.
Texto completoBertilsson, Tobias y Romario Johansson. "Undersökning om hjulmotorströmmar kan användas som alternativ metod för kollisiondetektering i autonoma gräsklippare. : Klassificering av hjulmotorströmmar med KNN och MLP". Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-43555.
Texto completoSyfte – Studiens syfte är att utöka kunskapen om hur hjulmotorstömmar kan kombineras med maskininlärning för att användas vid kollisionsdetektion hos autonoma robotar, detta för att kunna minska antalet krävda externa sensorer hos dessa robotar och på så sätt öppna upp design möjligheter samt minska produktionskostnader Metod – Studien genomfördes med design science research där två artefakter utvecklades i samarbete med Globe Tools Group. Artefakterna utvärderades sedan i hur de kategoriserade kollisioner utifrån en given datamängd som genererades från en autonom gräsklippare. Studiens experiment introducerade sedan in data som inte ingick i samma datamängd för att se hur metoderna kategoriserade detta. Resultat – Artefakterna klarade med 100% noggrannhet att detektera kollisioner i den giva datamängden som genererades. Dock har de två olika artefakterna olika beslutsregioner i hur de kategoriserar datamängderna till kollision samt icke-kollisioner, vilket kan ge dom olika användningsområden Implikationer – Examensarbetet bidrar till en ökad kunskap om hur maskininlärning och hjulmotorströmmar kan användas i ett kollisionsdetekteringssystem. Studiens resultat kan bidra till minskade kostnader i produktion samt nya design möjligheter Begränsningar – Datamängden som användes i studien samlades endast in av en autonom gräsklippare som gjorde frontalkrockar med underlaget konstgräs. Nyckelord – Maskininlärning, K-nearest neighbor, Multi-layer perceptron, kollisionsdetektion, autonoma robotar
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.
Texto completoGALLI, FABIAN. "Predicting PV self-consumption in villas with machine learning". Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300433.
Texto completoI Sverige finns ett starkt och växande intresse för solenergi. De senaste åren har antalet solcellsanläggningar ökat dramatiskt och en stor del är distribuerade nätanslutna solcellssystem, dvs takinstallationer. För närvarande är elexportpriset betydligt lägre än importpriset, vilket har gjort mängden egenanvänd solel till en kritisk faktor vid bedömningen av systemets lönsamhet. Egenanvändning (EA) beräknas med tidssteg upp till en timmes längd och är i hög grad beroende av solstrålningsmönstret för platsen av intresse, PV-systemkonfigurationen och byggnadens energibehov. Eftersom detta varierar för alla potentiella installationer är det svårt att göra uppskattningar utan att ha historiska data om både energibehov och lokal solstrålning, vilket ofta inte är tillgängligt. En metod för att förutsäga EA med allmän tillgänglig information är därför att föredra. Det finns en brist på dokumenterad EA-data och endast ett fåtal rapporter som behandlar kartläggning och prediktion av EA. I denna uppsats undersöks möjligheten att använda maskininlärning för att skapa modeller som kan förutsäga EA. De variabler som ingår är årlig energiförbrukning, årlig solcellsproduktion, lutningsvinkel och azimutvinkel för modulerna och latitud. Med programmeringsspråket Python skapas sju modeller med hjälp av olika regressionstekniker, där energiförbruknings- och simulerad solelproduktionsdata från södra Sverige används. Modellerna utvärderas med hjälp av determinationskoefficienten (R2) och mean absolute error (MAE). Teknikerna som används är linjär regression, polynomregression, Ridge regression, Lasso regression, K-nearest neighbor regression, Random Forest regression, Multi-Layer Perceptron regression. En additionell linjär regressions-modell skapas även med samma metodik som används i en tidigare publicerad rapport. En parametrisk analys av modellerna genomförs, där en variabel exkluderas åt gången för att bedöma modellens beroende av varje enskild variabel. Resultaten är mycket lovande, där fem av de åtta undersökta modeller uppnår ett R2-värde över 0,9. Den bästa modellen, Random Forest, har ett R2 på 0,985 och ett MAE på 0,0148. Den parametriska analysen visar också att även om ingångsdata är till hjälp, är det tillräckligt att använda årlig energiförbrukning och årlig solcellsproduktion för att göra bra förutsägelser. Det måste dock påpekas att modellprestandan endast är tillförlitlig för södra Sverige, från var beräkningsdata är hämtad, och inte tillämplig för områden utanför de valda latituderna eller land.
Börthas, Lovisa y 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.
Texto completoBehovet av att kunna modellera den verkliga vinsten av riktad marknadsföring har lett till den idag vanligt förekommande metoden inkrementell responsanalys. För att kunna utföra denna typ av metod krävs förekomsten av en existerande testgrupp samt kontrollgrupp och målet är således att beräkna differensen mellan de positiva utfallen i de två grupperna. Sannolikheten för de positiva utfallen för de två grupperna kan effektivt estimeras med statistiska maskininlärningsmetoder. De inkrementella responsanalysmetoderna som undersöks i detta projekt är subtraktion av två modeller, att modellera den inkrementella responsen direkt samt en klassvariabeltransformation. De statistiska maskininlärningsmetoderna som tillämpas är random forests och neurala nätverk samt standardmetoden logistisk regression. Datan är samlad från ett väletablerat detaljhandelsföretag och målet är därmed att undersöka vilken inkrementell responsanalysmetod och maskininlärningsmetod som presterar bäst givet datan i detta projekt. De mest avgörande aspekterna för att få ett bra resultat visade sig vara variabelselektionen och mängden kontrolldata i varje dataset. För att få ett lyckat resultat bör valet av maskininlärningsmetod vara random forests vilken används för att modellera den inkrementella responsen direkt, eller logistisk regression tillsammans med en klassvariabeltransformation. Neurala nätverksmetoder är känsliga för ojämna klassfördelningar och klarar därmed inte av att erhålla stabila modeller med den givna datan. Vidare presterade subtraktion av två modeller dåligt på grund av att var modell tenderade att fokusera för mycket på att modellera klassen i båda dataseten separat, istället för att modellera differensen mellan dem. Slutsatsen är således att en metod som modellerar den inkrementella responsen direkt samt en relativt stor kontrollgrupp är att föredra för att få ett stabilt resultat.
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/.
Texto completoSilva, Rodrigo Dalvit Carvalho da. "Um estudo sobre a extraÃÃo de caracterÃsticas e a classificaÃÃo de imagens invariantes à rotaÃÃo extraÃdas de um sensor industrial 3D". Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12154.
Texto completoNeste trabalho, à 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 à 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-Perceptron Multi-Camadas. Para a escolha do melhor extrator de caracterÃsticas, foram comparados os seus desempenhos de classificaÃÃo em termos de taxa de acerto e de tempo de extraÃÃo, atravÃs do classificador k-Vizinhos mais PrÃximos utilizando distÃncia euclidiana. O extrator de caracterÃsticas baseado nos momentos de Zernike obteve as melhores taxas de acerto, 98.00%, e tempo relativamente baixo de extraÃÃo de caracterÃsticas, 0.3910 segundos. Os dados gerados a partir deste, foram apresentados a diferentes heurÃsticas de classificaÃÃo. Dentre os classificadores testados, o classificador k-Vizinhos mais PrÃximos, obteve a melhor taxa mÃdia de acerto, 98.00% e, tempo mÃdio de classificaÃÃo relativamente baixo, 0.0040 segundos, tornando-se o classificador mais adequado para a aplicaÃÃo deste estudo.
In this work, the problem of recognition of objects using images extracted from a 3D industrial sensor is discussed. We focus in 9 feature extractors (where seven are based on invariant moments -Hu, Zernike, Legendre, Fourier-Mellin, Tchebichef, BesselâFourier and Gaussian-Hermite-, another is based on the Hough transform and the last one on independent component analysis), and 4 classifiers (Naive Bayes, k-Nearest Neighbor, Support Vector machines and Artificial Neural Network-Multi-Layer Perceptron). To choose the best feature extractor, their performance was compared in terms of classification accuracy rate and extraction time by the k-nearest neighbors classifier using euclidean distance. The feature extractor based on Zernike moments, got the best hit rates, 98.00 %, and relatively low time feature extraction, 0.3910 seconds. The data generated from this, were presented to different heuristic classification. Among the tested classifiers, the k-nearest neighbors classifier achieved the highest average hit rate, 98.00%, and average time of relatively low rank, 0.0040 seconds, thus making it the most suitable classifier for the implementation of this study.
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.
Texto completoThis work is based on the concept of neural networks committee machine and has the objective to solve the inverse radiative transfer problem in one-dimensional, homogeneous, absorbing and isotropic scattering media. The artificial neural networks committee machine adds and combines the knowledge acquired by an exact number of specialists which are represented, individually, by each one of the artificial neural networks (ANN) that composes the artificial neural network committee machine. The aim is to reach a final result better than the one obtained by any of the artificial neural network separately, selecting only those artificial neural networks that presents the best results during the generalization phase and discarding the others, what was done in this present work. Here are used two static models of committee machines, using the ensemble arithmetic average, that differ between themselves only by the composition of the output combinator by each one of the committee machine. Are obtained, using artificial neural networks committee machines, estimates for the radiative transfer parameters, that is, medium optical thickness, single scattering albedo and diffuse reflectivities. Finally, the results obtained with both models of committee machine are compared between themselves and with those found using artificial neural networks type multi-layer perceptrons (MLP), isolated. Here that artificial neural networks are named as specialists neural networks, showing that the technique employed brings performance and results improvements with relatively low computational cost.
Ak, Ronay. "Neural Network Modeling for Prediction under Uncertainty in Energy System Applications". Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0015/document.
Texto completoThis Ph.D. work addresses the problem of prediction within energy systems design and operation problems, and particularly the adequacy assessment of renewable power generation systems. The general aim is to develop an empirical modeling framework for providing predictions with the associated uncertainties. Along this research direction, a non-parametric, empirical approach to estimate neural network (NN)-based prediction intervals (PIs) has been developed, accounting for the uncertainty in the predictions due to the variability in the input data and the system behavior (e.g. due to the stochastic behavior of the renewable sources and of the energy demand by the loads), and to model approximation errors. A novel multi-objective framework for estimating NN-based PIs, optimal in terms of both accuracy (coverage probability) and informativeness (interval width) is proposed. Ensembling of individual NNs via two novel approaches is proposed as a way to increase the performance of the models. Applications on real case studies demonstrate the power of the proposed framework
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/.
Texto completoIn this study two methods of artificial intelligence (neural network and genetic algorithms) were used to work out a Chemosystematic study of the Asteraceae family. The family Asteraceae is one of the largest families among the Angiosperms, having about 24,000 species. The species of the family produce a large diversity of secondary metabolites, and some worth mentioning are the terpenoids, polyacetylenes, flavonoids and coumarins. For a better understanding of the chemical diversity of the family a database was built up with the occurrences of twelve classes of metabolites (monoterpenes, sesquiterpenes, lactonizadossesquiterpenes, diterpenes, triterpenes, coumarins, flavonoids, polyacetylenes, Benzofurans, benzopyrans, acetophenones and phenylpropanoids) produced by species of the family. From this database three different studies were conducted. In the first study, using the Kohonen self-organized map and the chemical data classified according to two of the most recent phylogenies of the family, it was possible to successfully separatethe tribes and genera of the Asteraceae family. It was also possible to indicate that the chemical information agrees with the phylogeny of Funk (Funk et al. 2009) than with the phylogeny of Bremer (Bremer 1994, 1996). In the next study, which aims at creating models to predict the number of occurrences of the twelve classes of metabolites using multi-layer perceptron with backpropagation algorithm error, the result was found unsatisfactory. Although in some classes of metabolites the training phase of the network has satisfactory results, the test phase showed that the models created are not able to make prevision for data to which they were submitted in the training phase and thus are not suitable models for predictions. Finally, the third study was the creation of linear regression models using a genetic algorithm method of variable selection. This study could indicate that the monoterpenes and sesquiterpenes are closely related biosynthetically, and was also possible to indicate that there are biosynthetic relations between monoterpenes and diterpenes and between sesquiterpenes and triterpenes
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.
Texto completoBügner, 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/.
Texto completoDie 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 vorhandenen linearen Lösungsansätzen werden in dieser Arbeit Methoden aus dem Bereich der Neuronalen Netzwerke eingesetzt. Diese nichtlinearen Diagnoseverfahren sind besonders geeignet für die Analyse von Prozessabläufen, wie sie beispielsweise im Training vorliegen.
Im theoretischen Teil werden zunächst Gemeinsamkeiten, Abhängigkeiten und Unterschiede in den Bereichen Training, Wettkampf und Leistungsfähigkeit untersucht sowie die Brücke zwischen trainingswissenschaftlicher Diagnostik und nichtlinearen Verfahren über die Begriffe der Interdisziplinarität und Integrativität geschlagen. Angelehnt an die Theorie der Neuronalen Netze werden anschließend die Grundlagenmodelle Perzeptron, Multilayer-Perzeptron und Selbstorganisierende Karten theoretisch erläutert. Im empirischen Teil stehen dann die nichtlineare Analyse von personalen Anforderungsstrukturen, Zustände der sportlichen Form und die Prognose sportlichen Talents - allesamt bei jugendlichen Leistungsschwimmerinnen und -schwimmern - im Mittelpunkt. Die nichtlinearen Methoden werden dabei einerseits auf ihre wissenschaftliche Aussagekraft überprüft, andererseits untereinander sowie mit linearen Verfahren verglichen.
The diagnostic methods in training science concentrate on the core areas of training, competition, and performance. The methods commonly used are characterized by a high degree of practical applicability and distinct structural complexity. These characteristics have led to the question which scientific methods fit best for resolving problems like, for example, the optimization of athletic performance, efficient planning and monitoring of training processes, effective talent screening, selection and development, or the formation of analytical models. All these questions have not yet been answered sufficiently.
Aside from the traditional mathematical approaches on the basis of the linear model, nonlinear methods in the field of neural networks are used in this dissertation. These nonlinear diagnostic methods are especially suitable for the analysis of coherent patterns in time series such as training processes.
In the theoretical part of the dissertation, common aspects, mutual dependencies, and differences between training, competition, and performance are examined. In this context, a bridge is built between the diagnostic purposes in these fields and suitable nonlinear methods. Along the lines of the neural networks theory, the basic models Perceptron, Multilayer-Perceptron, and Self-Organizing Feature Maps are subsequently elucidated.
In the empirical part of the thesis, three studies conducted with top level adolescent swimmers are presented that focus on the nonlinear analysis of personal athletic ability structures, different states of athletic shape, and the prognosis of athletic talent. The nonlinear methods are thus examined as to how worthwhile they are for analytical purposes in training science on the one hand, and they are compared to each other as well as to linear methods on the other hand.
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.
Texto completoElspotspriser är svåra att förutsäga eftersom de beror på olika instabila och oregelbundna faktorer, och också på grund av att elektricitet är en vara som inte kan lagras effektivt. Detta leder till ett volatilt, fluktuerande beteende hos priserna, med många plötsliga toppar. Maskininlärningsalgoritmer har överträffat traditionella metoder inom olika områden på grund av deras förmåga att lära sig komplexa mönster. Under det senaste decenniet har djupinlärningsmetoder introducerats till problem inom elprisprognostisering och ofta visat sig överlägsna sina föregångare. I denna avhandling konstruerades och utvärderades flera djupinlärningsmodeller på deras förmåga att förutsäga spotpriserna 10 dagar framåt. Den första slutsatsen är att relativt simpla nätverksarkitekturer kan förutsäga priser med hög noggrannhet, förutom för fallen med de mest extrema, plötsliga topparna. Vidare, så övertränade alla djupa neurala nätverken den statistiska modellen som användes som riktmärke. Slutligen, så gav de föreslagna LSTM- och CNN-modellerna prognoser som var statistiskt, signifikant överlägsna de andra och hade de lägsta felen, vilket tyder på att de är bäst lämpade för prognostiseringsuppgiften.
Khan, Muhammad Alam Z. "Transient engine model for calibration using two-stage regression approach". Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/8456.
Texto completoSonnert, 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.
Texto completoMuñoz, 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.
Texto completoEsta tesis se centra en el análisis comprensivo de las capacidades de algunos tipos de Red Neuronal Artificial aún no testados: las Redes Neuronales Probabilísticas (PNN) y los Conjuntos de Perceptrones Multicapa (MLP Ensembles). Los análisis sobre las capacidades de estas técnicas se desarrollaron utilizando la trucha común (Salmo trutta; Linnaeus, 1758), la bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) y el barbo colirrojo (Barbus haasi; Mertens, 1925) como especies nativas objetivo. Los análisis se centraron en la capacidad de predicción, la interpretabilidad de los modelos y el efecto del exceso de ceros en las bases de datos de entrenamiento, la así llamada prevalencia de los datos (i.e. la proporción de casos de presencia sobre el conjunto total). Finalmente, el efecto de la escala (micro-escala o escala de microhábitat y meso-escala) en los modelos de idoneidad del hábitat y consecuentemente en la evaluación de caudales ambientales se estudió en el último capítulo.
Aquesta tesis se centra en l'anàlisi comprensiu de les capacitats d'alguns tipus de Xarxa Neuronal Artificial que encara no han estat testats: les Xarxes Neuronal Probabilístiques (PNN) i els Conjunts de Perceptrons Multicapa (MLP Ensembles). Les anàlisis sobre les capacitats d'aquestes tècniques es varen desenvolupar emprant la truita comuna (Salmo trutta; Linnaeus, 1758), la madrilla roja (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) i el barb cua-roig (Barbus haasi; Mertens, 1925) com a especies objecte d'estudi. Les anàlisi se centraren en la capacitat predictiva, interpretabilitat dels models i en l'efecte de l'excés de zeros a la base de dades d'entrenament, l'anomenada prevalença de les dades (i.e. la proporció de casos de presència sobre el conjunt total). Finalment, l'efecte de la escala (micro-escala o microhàbitat i meso-escala) en els models d'idoneïtat de l'hàbitat i conseqüentment en l'avaluació de cabals ambientals es va estudiar a l'últim capítol.
Muñoz Mas, R. (2016). Multivariate approaches in species distribution modelling: Application to native fish species in Mediterranean Rivers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/76168
TESIS
Janda, Miloš. "Detekce hran pomocí neuronové sítě". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237175.
Texto completo