Дисертації з теми "Adaptive machine learning"
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Jelfs, Beth. "Collaborative adaptive filtering for machine learning." Thesis, Imperial College London, 2009. http://hdl.handle.net/10044/1/5598.
Повний текст джерелаMiles, Jonathan David. "Machine Learning for Adaptive Computer Game Opponents." The University of Waikato, 2009. http://hdl.handle.net/10289/2779.
Повний текст джерелаLong, Shun. "Adaptive Java optimisation using machine learning techniques." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/567.
Повний текст джерелаAr, Rosyid Harits. "Adaptive serious educational games using machine learning." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-serious-educational-games-using-machine-learning(b5f5024b-c7fd-4660-997c-9fd22e140a8f).html.
Повний текст джерелаDal, Pozzolo Andrea. "Adaptive Machine Learning for Credit Card Fraud Detection." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/221654.
Повний текст джерелаDoctorat en Sciences
info:eu-repo/semantics/nonPublished
Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.
Повний текст джерелаCan computers teach people? To answer this question, Intelligent Tutoring Systems are a rapidly expanding field of research among the Information and Communication Technologies for the Education community. This subject brings together different issues and researchers from various fields, such as psychology, didactics, neurosciences and, particularly, machine learning. Digital technologies are becoming more and more a part of everyday life with the development of tablets and smartphones. It seems natural to consider using these technologies for educational purposes. This raises several questions, such as how to make user interfaces accessible to everyone, how to make educational content motivating and how to customize it to individual learners. In this PhD, we developed methods, grouped in the aptly-named HMABITS framework, to adapt pedagogical activity sequences based on learners' performances and preferences to maximize their learning speed and motivation. These methods use computational models of intrinsic motivation and curiosity-driven learning to identify the activities providing the highest learning progress and use Multi-Armed Bandit algorithms to manage the exploration/exploitation trade-off inside the activity space. Activities of optimal interest are thus privileged with the target to keep the learner in a state of Flow or in his or her Zone of Proximal Development. Moreover, some of our methods allow the student to make choices about contextual features or pedagogical content, which is a vector of self-determination and motivation. To evaluate the effectiveness and relevance of our algorithms, we carried out several types of experiments. We first evaluated these methods with numerical simulations before applying them to real teaching conditions. To do this, we developed multiple models of learners, since a single model never exactly replicates the behavior of a real learner. The simulation results show the HMABITS framework achieves comparable, and in some cases better, learning results than an optimal solution or an expert sequence. We then developed our own pedagogical scenario and serious game to test our algorithms in classrooms with real students. We developed a game on the theme of number decomposition, through the manipulation of money, for children aged 6 to 8. We then worked with the educational institutions and several schools in the Bordeaux school district. Overall, about 1000 students participated in trial lessons using the tablet application. The results of the real-world studies show that the HMABITS framework allows the students to do more diverse and difficult activities, to achieve better learning and to be more motivated than with an Expert Sequence. The results show that this effect is even greater when the students have the possibility to make choices
Wiens, Jenna Marleau. "Machine learning for patient-adaptive ectopic beat classification." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60823.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (p. 83-85).
Physicians require automated techniques to accurately analyze the vast amount of physiological data collected by continuous monitoring devices. In this thesis, we consider one analysis task in particular, the classification of heartbeats from electrocardiographic recordings (ECG). This problem is made challenging by the inter-patient differences present in ECG morphology and timing characteristics. Supervised classifiers trained on a collection of patients can have unpredictable results when applied to a new patient. To reduce the effect of inter-patient differences, researchers have suggested training patient-adative classifiers by training on labeled data from the test patient. However, patient-adaptive classifiers have not been integrated in practice because they require an impractical amount of patient-specific expert knowledge. We present two approaches based on machine learning for building accurate patientadaptive beat classifiers that use little or no patient-specific expert knowledge. First, we present a method to transfer and adapt knowledge from a collection of patients to a test-patient. This first approach, based on transductive transfer learning, requires no patient-specific labeled data, only labeled data from other patients. Second, we consider the scenario where patient-specific expert knowledge is available, but comes at a high cost. We present a novel algorithm for SVM active learning. By intelligently selecting the training set we show how one can build highly accurate patient-adaptive classifiers using only a small number of cardiologist supplied labels. Our results show the gains in performance possible when using patient-adaptive classifiers in place of global classifiers. Furthermore, the effectiveness of our techniques, which use little or no patient-specific expert knowledge, suggest that it is also practical to use patient-adaptive techniques in a clinical setting.
by Jenna Marleau Wiens.
S.M.
Drolia, Utsav. "Adaptive Distributed Caching for Scalable Machine Learning Services." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1004.
Повний текст джерелаYin, Wenjie. "Machine Learning for Adaptive Cruise Control Target Selection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264918.
Повний текст джерелаFordon kommer att vara mer komplexa, säkra och intelligenta i framtiden. Till exempel, med stöd av det avancerade förarassistanssystemet (ADAS) kan föraren och passagerarnas säkerhet och komfort förbättras avsevärt. Detta examensarbete föreslår datastyrda lösningar för målval för adaptivt fartreglering (ACC) för att välja ett av föregående fordon som det primära målet. Valet liknar det som människor gör. Arbetet genomfördes i samarbete med Scania CV AB. Ett delat nätverk och ett gemensamt LSTM-nätverk användes för att välja det primära målet. Dessutom har en ny maskinbaserad målvalsmodell (jämförelse-målmodell) utformats, vilken kan överväga alla närliggande fordon tillsammans genom att jämföra fordon. Ett jämför-mål-nätverk och ett jämförbart mål XGBoost utvecklas baserat på jämförelsemodellen. Totalt användes fyra olika maskininlärningsmetoder för att välja det primara målet för ACC, inklusive ett delat nätverk, ett gemensamt LSTM-nätverk, ett jämförelsemål-nätverk och en jämförbar XGBoost-modell. Dessa meto- der jämfördes och analyserades. Finjustering antogs för att motverka dataobalansproblemet för sällsynta situationer. Jämförelse-målet XGBoost kan uppnå 94.85% noggrannhet på testuppsättningen.
Grundtman, Per. "Adaptive Learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648.
Повний текст джерелаEmani, Murali Krishna. "Adaptive parallelism mapping in dynamic environments using machine learning." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10469.
Повний текст джерелаSpronck, Pieter Hubert Marie. "Adaptive game AI." [Maastricht] : Maastricht : UPM, Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 2005. http://arno.unimaas.nl/show.cgi?fid=5330.
Повний текст джерелаPerumalla, Calvin A. "Machine Learning and Adaptive Signal Processing Methods for Electrocardiography Applications." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6926.
Повний текст джерелаHolley, Julian. "Oneiric Machine Learning : The Foundations of Dream Inspired Adaptive Systems." Thesis, University of the West of England, Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495516.
Повний текст джерелаZhou, Lily M. Eng Massachusetts Institute of Technology. "Paper Dreams : an adaptive drawing canvas supported by machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122990.
Повний текст джерелаThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 55-58).
Despite numerous recent advances in the field of deep learning for artistic purposes, the integration of these state-of-the-art machine learning tools into applications for drawing and visual expression has been an underexplored field. Bridging this gap has the potential to empower a large subset of the population, from children to the elderly, with a new medium to represent and visualize their ideas. Paper Dreams is a web-based canvas for sketching and storyboarding, with a multimodal user interface integrated with a variety of machine learning models. By using sketch recognition, style transfer, and natural language processing, the system can contextualize what the user is drawing; it then can color the sketch appropriately, suggest related objects for the user to draw, and allow the user to pull from a database of related images to add onto the canvas. Furthermore, the user can influence the output of the models via a serendipity dial that affects how "wacky" the system's outputs are. By processing a variety of multimodal inputs and automating artistic processes, Paper Dreams becomes an efficient tool for quickly generating vibrant and complex artistic scenes.
by Lily Zhou.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Negus, Andra Stefania. "Adaptive Anomaly Detection for Large IoT Datasets with Machine Learning and Transfer Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426257.
Повний текст джерелаNya IoT-enheter träder in på marknaden så det blir allt viktigare att utveckla tillförlitliga och anpassningsbara sätt att hantera de data de genererar. Dessa bör hantera datakvalitet och tillförlitlig- het. Sådana lösningar kan gynna båda tillverkarna av apparater och deras kunder som som ett resultat kan dra nytta av snabbare och bättre kundsupport / tjänster. Således har detta projekt två mål. Det första är att identifiera felaktiga datapunkter som genereras av sådana enheter. För det andra är det att utvärdera om kunskapen från tillgängliga / kända sensorer och apparater kan överföras till andra sensorer på liknande enheter. Detta skulle göra det möjligt att utvärdera beteendet hos nya apparater så snart de slås på första gången, snarare än efter att tillräcklig information från dem har samlats in. Detta projekt använder tidsseriedata från tre apparater: tvättmaskin, tvättmaskin och torktumlare och kylskåp. För dessa utvecklas och testas två lösningar: en för kategoriska variabler och en annan för numeriska variabler. De kategoriska variablerna analyseras med två metoder: Average Value Frequency och den rena frekvensen för tillståndsövergång. På grund av det begränsade antalet möjliga tillstånd visar sig den rena frekvensen vara den bättre lösningen, och kunskapen som erhålls överförs från källanordningen till målet, med måttlig framgång. De numeriska variablerna analyseras med hjälp av en One-class Support Vector Machine-pipeline, med mycket lovande resultat. Vidare utvecklas inlärnings- och glömningsmekanismer för att möjliggöra för rörledningarna att anpassa sig till förändringar i apparatens beteendemönster. Detta inkluderar en sönderfallningsfunktion för den numeriska variabellösningen. Intressant är att de olika vikterna för källan och målet har liten eller ingen inverkan på kvaliteten på klassificeringen.
Smith, Adalet Serengül Güven. "Application of machine learning algorithms in adaptive web-based information systems." Thesis, Middlesex University, 1999. http://eprints.mdx.ac.uk/6743/.
Повний текст джерелаEwert, Kevin. "An Adaptive Machine Learning Approach to Knowledge Discovery in Large Datasets." NSUWorks, 2006. http://nsuworks.nova.edu/gscis_etd/510.
Повний текст джерелаChen, Guoyu. "PAILAC: Power and Inference Latency Adaptive Control for Machine Learning Services." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu160608666572472.
Повний текст джерелаCastaño-Candamil, Sebastián [Verfasser], and Michael W. [Akademischer Betreuer] Tangermann. "Machine learning methods for motor performance decoding in adaptive deep brain stimulation." Freiburg : Universität, 2020. http://d-nb.info/1224808762/34.
Повний текст джерелаBawaskar, Neerja Pramod. "Analog Implicit Functional Testing using Supervised Machine Learning." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/2099.
Повний текст джерелаStackhouse, Christian Paul 1960. "AN ADAPTIVE RULE-BASED SYSTEM." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276534.
Повний текст джерелаDi, Yuan. "Enhanced System Health Assessment using Adaptive Self-Learning Techniques." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420412871182.
Повний текст джерелаWang, Olivier. "Adaptive Rules Model : Statistical Learning for Rule-Based Systems." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX037/document.
Повний текст джерелаBusiness Rules (BRs) are a commonly used tool in industry for the automation of repetitive decisions. The emerging problem of adapting existing sets of BRs to an ever-changing environment is the motivation for this thesis. Existing Supervised Machine Learning techniques can be used when the adaptation is done knowing in detail which is the correct decision for each circumstance. However, there is currently no algorithm, theoretical or practical, which can solve this problem when the known information is statistical in nature, as is the case for a bank wishing to control the proportion of loan requests its automated decision service forwards to human experts. We study the specific learning problem where the aim is to adjust the BRs so that the decisions are close to a given average value.To do so, we consider sets of Business Rules as programs. After formalizing some definitions and notations in Chapter 2, the BR programming language defined this way is studied in Chapter 3, which proves that there exists no algorithm to learn Business Rules with a statistical goal in the general case. We then restrain the scope to two common cases where BRs are limited in some way: the Iteration Bounded case in which no matter the input, the number of rules executed when taking the decision is less than a given bound; and the Linear Iteration Bounded case in which rules are also all written in Linear form. In those two cases, we later produce a learning algorithm based on Mathematical Programming which can solve this problem. We briefly extend this theory and algorithm to other statistical goal learning problems in Chapter 5, before presenting the experimental results of this thesis in Chapter 6. The last includes a proof of concept to automate the main part of the learning algorithm which does not consist in solving a Mathematical Programming problem, as well as some experimental evidence of the computational complexity of the algorithm
Vartak, Aniket. "BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2667.
Повний текст джерелаPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
Kochenderfer, Mykel J. "Adaptive modelling and planning for learning intelligent behaviour." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/1408.
Повний текст джерелаMena-Yedra, Rafael. "An adaptive, fault-tolerant system for road network traffic prediction using machine learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669802.
Повний текст джерелаEsta tesis ha abordado el diseño y desarrollo de un sistema integrado para la predicción de tráfico en tiempo real basándose en métodos de aprendizaje automático. Aunque la predicción de tráfico ha sido la motivación que ha guiado el desarrollo de la tesis, gran parte de las ideas y aportaciones científicas propuestas en esta tesis son lo suficientemente genéricas como para ser aplicadas en cualquier otro problema en el que, idealmente, su definición sea la del flujo de información en una estructura de grafo. Esta aplicación es de especial interés en entornos susceptibles a cambios en el proceso de generación de datos. Además, la arquitectura modular facilita la adaptación a una gama más amplia de problemas. Por otra parte, ciertas partes específicas de esta tesis están fuertemente ligadas a la teoría del flujo de tráfico. El enfoque de esta tesis se centra en una perspectiva macroscópica del flujo de tráfico en la que los flujos individuales están ligados a la demanda de tráfico subyacente. Las predicciones a corto plazo incluyen la caracterización de las carreteras en base a las medidas de tráfico -flujo, densidad y/o velocidad-, el estado del tráfico -si la carretera está congestionada o no, y su severidad-, y la detección de condiciones anómalas -incidentes u otros eventos no recurrentes-. Los datos utilizados en esta tesis proceden de detectores instalados a lo largo de las redes de carreteras. No obstante, otros tipos de fuentes de datos podrían ser igualmente empleados con el preprocesamiento apropiado. Esta tesis ha sido desarrollada en el contexto de Aimsun Live -software desarrollado por Aimsun, basado en simulación para la predicción en tiempo real de tráfico-. Los métodos aquí propuestos cooperarán con este. Un ejemplo es cuando se detecta un incidente o un evento no recurrente, entonces pueden simularse diferentes estrategias para medir su impacto. Parte de esta tesis también ha sido desarrollada en el marco del proyecto de la UE "SETA" (H2020-ICT-2015). La principal motivación que ha guiado el desarrollo de esta tesis es mejorar aquellas limitaciones previamente identificadas en Aimsun Live, y cuya investigación encontrada en la literatura no ha sido muy extensa. Estos incluyen: -Autonomía, tanto en la etapa de preparación como en la de tiempo real. -Adaptación, a los cambios graduales o abruptos de la demanda u oferta de tráfico. -Sistema informativo, sobre las condiciones anómalas de la carretera. -Mejora en la precisión de las predicciones con respecto a la metodología anterior de Aimsun y a un método típico usado como referencia. -Robustez, para hacer frente a datos defectuosos o faltantes en tiempo real. -Interpretabilidad, adoptando criterios de modelización hacia un razonamiento más transparente para un humano. -Escalable, utilizando una arquitectura modular con énfasis en una explotación paralela de grandes cantidades de datos. El resultado de esta tesis es un sistema integrado –Adarules- para la predicción en tiempo real que sabe maximizar el provecho de los datos históricos disponibles, mientras que al mismo tiempo también sabe aprovechar el tamaño teórico ilimitado de los datos en un escenario de streaming. Esto se logra a través del aprendizaje en línea y la capacidad de detección de cambios junto con la búsqueda automática y el mantenimiento de los patrones en la estructura de grafo de la red. Además del sistema Adarules, otro resultado de la tesis es un modelo probabilístico que caracteriza un conjunto de variables latentes interpretables relacionadas con el estado del tráfico basado en los datos de sensores junto con el conocimiento previo –opcional- proporcionado por el experto en tráfico utilizando un planteamiento Bayesiano. Sobre este modelo de estados de tráfico se construye el modelo espacio-temporal probabilístico que aprende la dinámica de la transición de estados
Tysk, Carl, and Jonathan Sundell. "Adaptive detection of anomalies in the Saab Gripen fuel tanks using machine learning." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414208.
Повний текст джерелаLi, Max Hongming. "Extension on Adaptive MAC Protocol for Space Communications." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1275.
Повний текст джерелаBridle, Robert Angus, and robert bridle@gmail com. "Adaptive User Interfaces for Mobile Computing Devices." The Australian National University. College of Engineering and Computer Sciences, 2008. http://thesis.anu.edu.au./public/adt-ANU20081117.184430.
Повний текст джерелаBridle, Robert Angus. "Adaptive user interfaces for mobile computing devices /." View thesis entry in Australian Digital Theses Program, 2008. http://thesis.anu.edu.au/public/adt-ANU20081117.184430/index.html.
Повний текст джерелаLesh, Neal. "Scalable and adaptive goal recognition /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/6897.
Повний текст джерелаWright, Hamish Michael. "A Homogeneous Hierarchical Scripted Vector Classification Network with Optimisation by Genetic Algorithm." Thesis, University of Canterbury. Electrical and Computer Engineering, 2007. http://hdl.handle.net/10092/1191.
Повний текст джерелаErdogmus, Deniz. "Information theoretic learning Renyi's entropy and its applications to adaptive system training /." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1000122.
Повний текст джерелаTitle from title page of source document. Document formatted into pages; contains xv, 217 p.; also contains graphics. Includes vita. Includes bibliographical references.
Stone, Erik E. Skubic Marge. "Adaptive temporal difference learning of spatial memory in the water maze task." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6586.
Повний текст джерелаVong, Chi Man. "Integrated machine learning techniques with application to adaptive decision support system for automotive engineering." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1637079.
Повний текст джерелаBooth, Ash. "Automated algorithmic trading : machine learning and agent-based modelling in complex adaptive financial markets." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/397453/.
Повний текст джерелаBai, Bing. "A Study of Adaptive Random Features Models in Machine Learning based on Metropolis Sampling." Thesis, KTH, Numerisk analys, NA, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293323.
Повний текст джерелаI artificiella neurala nätverk (ANN), som används inom maskininlärning, behöver parametrar, kallade frekvensparametrar och amplitudparametrar, hittasgenom en så kallad träningsprocess. Random feature-modeller är ett specialfall av ANN där träningen sker på ett annat sätt. I dessa modeller tränasamplitudparametrarna medan frekvensparametrarna samplas från någon sannolikhetstäthet. Om denna sannolikhetstäthet valts med omsorg kommer båda träningsmodellerna att ge god approximation av givna data. Metoden Adaptiv random Fourier feature[1] uppdaterar frekvensfördelningen adaptivt. Denna uppsats studerar aktiveringsfunktionerna ReLU och sigmoid och kombinerar dem med den adaptiva iden i [1] för att generera två ytterligare Random feature-modeller. Resultaten visar att om samma hyperparametrar som i [1] används så kan den adaptiva ReLU features-modellen approximera data relativt väl, även om Fourier features-modellen ger något bättre resultat.
Reichstaller, Andre [Verfasser], and Alexander [Akademischer Betreuer] Knapp. "Machine Learning-based Test Strategies for Self-Adaptive Systems / Andre Reichstaller ; Betreuer: Alexander Knapp." Augsburg : Universität Augsburg, 2020. http://d-nb.info/1225683254/34.
Повний текст джерелаLacaze, Sylvain. "Active Machine Learning for Computational Design and Analysis under Uncertainties." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556446.
Повний текст джерелаPérez, Culubret Adrià 1993. "Learning how to simulate : Applying machine learning methods to improve molecular dynamics simulations." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2022. http://hdl.handle.net/10803/673392.
Повний текст джерелаJoe-Yen, Stefan. "Performance Envelopes of Adaptive Ensemble Data Stream Classifiers." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1014.
Повний текст джерелаJordaan, Edzard Adolf Biermann. "Intelligent elevator control based on adaptive learning and optimisation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95999.
Повний текст джерелаENGLISH ABSTRACT: Machine learning techniques have been around for a few decades now and are being established as a pre-dominant feature in most control applications. Elevators create a unique control application where traffic flow is controlled and directed according to certain control philosophies. Machine learning techniques can be implemented to predict and control every possible traffic flow scenario and deliver the best possible solution. Various techniques will be implemented in the elevator application in an attempt to establish a degree of artificial intelligence in the decision making process and to be able to have increased interaction with the passengers at all times. The primary objective for this thesis is to investigate the potential of machine learning solutions and the relevancy of such technologies in elevator control applications. The aim is to establish how the research field of machine learning, specifically neural network science, can be successfully utilised with the goal of creating an artificial intelligent (AI) controller. The AI controller is to adapt to its existing state and change its control parameters as required without the intervention of the user. The secondary objective for this thesis is to develop an elevator model that represents every aspect of the real-world application. The purpose of the model is to improve the accuracy of existing theoretical and simulated models, by modulating previously unknown and complex variables and constraints. The aim is to create a complete and fully functional testing platform for developing new elevator control philosophies and testing new elevator control mechanisms. To achieve these objectives, the main focus is directed to how waiting time, probability theory and power consumption predictions can be optimally utilised by means of machine learning solutions. The theoretical background is provided for these concepts and how each subject can potentially influence the decision making process. The reason why this approach has been difficult to implement in the past, is possibly mainly due to the lack of adequate representation for these concepts in an online environment without the continuous feedback from an Expert System. As a result of this thesis, the respective online models for each of these concepts were successfully developed in order to deal with the identified shortcomings. The developed online models for projected waiting times, probability networks and power consumption feedback were then combined to form a new Intelligent Elevator Controller (IEC) structure as opposed to the Expert System approach, mostly used in present computer based elevator controllers.
AFRIKAANSE OPSOMMING: Masjienleertegnieke bestaan al vir 'n paar dekades en is 'n oorwegende kenmerk in hedendaagse beheertoestelle. Hysbakke skep 'n unieke beheertoepassing, waar verkeersvloei beheer en gerig kan word volgens sekere beheer loso e. Masjienleertegnieke kan geïmplementeer word om elke moontlike verkeersvloei situasie te voorspel en te beheer en die beste moontlike oplossing te lewer. Verskeie tegnieke sal in die tesis ondersoek word in 'n poging om 'n mate van kunsmatige intelligensie in die besluitneming proses te skep asook verhoogte interaksie met die passasiers te alle tye. Die prim^ere doel van hierdie tesis is om die potensiaal van 'n masjienleer oplossing en die toepaslikheid van dit in hysbakbeheertoepassings te ondersoek. Die doel is om vas te stel hoe die navorsing in die veld van die masjienleer, spesi ek in neurale netwerk wetenskappe, suksesvol aangewend kan word met die doel om 'n kunsmatige intelligente beheerder te skep. Die kunsmatige intelligente beheerder moet kan aanpas by sy onmidelike omgewing en sy beheer parameters moet kan verander soos nodig sonder die ingryping van die gebruiker. Die sekond^ere doelwit vir hierdie tesis is om 'n hysbakmodel, wat elke aspek van die werklike w^ereld verteenwoordig, te ontwikkel. Die doel van die model is om die akkuraatheid van die bestaande teoretiese en gesimuleerde modelle te verbeter deur voorheen onbekende en komplekse veranderlikes en beperkings in ag te neem. Die doel is om 'n funksionele toetsplatform te skep vir die ontwikkeling van nuwe hysbakbeheer loso e en vir die toets van nuwe hysbakbeheermeganismes. Om hierdie doelwitte te bereik, is die hoo okus gerig om wagtyd, waarskynlikheidsteorie en kragverbruik voorspellings optimaal te gebruik deur middel van die masjienleer oplossings. Die teoretiese agtergrond is voorsien vir hierdie konsepte en hoe elke konsep potensieel die besluitneming kan beïnvloed. Die rede waarom hierdie benadering moeilik was om te implementeer tot hede, is moontlik te wyte aan die gebrek aan voldoende verteenwoordiging vir hierdie konsepte in 'n aanlynomgewing sonder die voortdurende terugvoer van 'n Deskundige Stelsel. As gevolg van hierdie tesis word die onderskeie aanlynmodelle vir elk van hierdie konsepte suksesvol ontwikkel om die geïdenti seerde tekortkominge te oorkom. Die ontwikkelde aanlynmodelle vir geprojekteerde wagtye, waarskynlikheidsnetwerke en kragverbruik terugvoer is dan gekombineer om 'n nuwe intelligente hysbakbeheerder struktuur te skep, in teenstelling met die Deskundige Stelsel benadering in die huidige rekenaar gebaseerde hysbakbeheerders.
Maus, Rickard, and Mattias Arvidsson. "Predicting Parameters of Adaptive Integrate-and-Fire Models through Machine Learning with Gramian Angular Fields." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301737.
Повний текст джерелаInom området av neurovetenskap är simulering av neuroner och neuronala nätverk ofta av stort intresse. Innan neuronmodellerna kan användas krävs det justering av flera parametrar för att korrekt replikera egenskaper hos en given neurontyp. Det finns flera metoder för att göra denna justering av parametrar men de har ett vanligt problem, de är beräkningstunga. I ett försök att minska den beräkningskostnad föreslår vi i denna studie en tillämpning av ’Convolutional Neural Networks’ med ’Gramian Angular Fields’ av spännings ’traces’ för att optimera parametrar med regression. Efter att ha tränat och evaluerat nätverket på data genererad från AdEx modellen i NEST fann vi att ’Convolutional Neural Networks’ i samband med ’Gramian Angular Fields’ fungerar exceptionellt bra på syntetisk data. Modellen kunde förutsäga alla utom en parameter med nästan alla återskapade ’traces’ inom acceptabla felintervall. Resultaten är lovande, men studien baserades enbart på syntetisk data. Framtida arbete med experimentella data är därför nödvändigt för att examinera metodens fulla förmåga.
Buttar, Sarpreet Singh. "Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-77201.
Повний текст джерелаSouriau, Rémi. "machine learning for modeling dynamic stochastic systems : application to adaptive control on deep-brain stimulation." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG004.
Повний текст джерелаThe past recent years have been marked by the emergence of a large amount of database in many fields like health. The creation of many databases paves the way to new applications. Properties of data are sometimes complex (non linearity, dynamic, high dimensions) and require to perform machine learning models. Belong existing machine learning models, artificial neural network got a large success since the last decades. The success of these models lies on the non linearity behavior of neurons, the use of latent units and the flexibility of these models to adapt to many different problems. Boltzmann machines presented in this thesis are a family of generative neural networks. Introduced by Hinton in the 80's, this family have got a large interest at the beginning of the 21st century and new extensions are regularly proposed.This thesis is divided into two parts. A first part exploring Boltzmann machines and their applications. In this thesis the unsupervised learning of intracranial electroencephalogram signals on rats with Parkinson's disease for the control of the symptoms is studied.Boltzmann machines gave birth to Diffusion networks which are also generative model based on the learning of a stochastic differential equation for dynamic and stochastic data. This model is studied again in this thesis and a new training algorithm is proposed. Its use is tested on toy data as well as on real database
Hartness, Ken T. N. "Adaptive Planning and Prediction in Agent-Supported Distributed Collaboration." Thesis, University of North Texas, 2004. https://digital.library.unt.edu/ark:/67531/metadc4702/.
Повний текст джерелаMancini, Riccardo. "Optimizing cardboard-blank picking in a packaging machine by using Reinforcement Learning algorithms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
Знайти повний текст джерелаKaylani, Assem. "AN ADAPTIVE MULTIOBJECTIVE EVOLUTIONARY APPROACH TO OPTIMIZE ARTMAP NEURAL NETWORKS." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2538.
Повний текст джерелаPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering PhD
Salem, Maher [Verfasser]. "Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques / Maher Salem." Kassel : Universitätsbibliothek Kassel, 2014. http://d-nb.info/1060417847/34.
Повний текст джерела