Дисертації з теми "Automatic classification Statistical methods"
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Latino, Diogo Alexandre Rosa Serra. "Automatic learning for the classification of chemical reactions and in statistical thermodynamics." Doctoral thesis, FCT - UNL, 2008. http://hdl.handle.net/10362/1752.
Повний текст джерелаArshad, Irshad Ahmad. "Using statistical methods for automatic classifications of clouds in ground-based photographs of the sky." Thesis, University of Essex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250129.
Повний текст джерелаShepherd, Gareth William Safety Science Faculty of Science UNSW. "Automating the aetiological classification of descriptive injury data." Awarded by:University of New South Wales. School of Safety Science, 2006. http://handle.unsw.edu.au/1959.4/24934.
Повний текст джерелаMonroy, Chora Isaac. "An investigation on automatic systems for fault diagnosis in chemical processes." Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/77637.
Повний текст джерелаLa seguridad de planta es el problema más inquietante para las industrias químicas. Un fallo en planta puede causar pérdidas económicas y daños humanos y al medio ambiente. La mayoría de los fallos operacionales son previstos en la etapa de diseño de un proceso mediante la aplicación de técnicas de Análisis de Riesgos y de Operabilidad (HAZOP). Sin embargo, existe la probabilidad de que pueda originarse un fallo en una planta en operación. Por esta razón, es de suma importancia que una planta pueda detectar y diagnosticar fallos en el proceso y tomar las medidas correctoras adecuadas para mitigar los efectos del fallo y evitar lamentables consecuencias. Es entonces también importante el mantenimiento preventivo para aumentar la seguridad y prevenir la ocurrencia de fallos. La diagnosis de fallos ha sido abordada tanto con modelos analíticos como con modelos basados en datos y usando varios tipos de técnicas y algoritmos. Sin embargo, hasta ahora no existe la propuesta de un sistema general de seguridad en planta que combine detección y diagnosis de fallos ya sea registrados o no registrados anteriormente. Menos aún se han reportado metodologías que puedan ser automatizadas e implementadas en la práctica real. Con la finalidad de abordar el problema de la seguridad en plantas químicas, esta tesis propone un sistema general para la detección y diagnosis de fallos capaz de implementarse de forma automatizada en cualquier industria. El principal requerimiento para la construcción de este sistema es la existencia de datos históricos de planta sin previo filtrado. En este sentido, diferentes métodos basados en datos son aplicados como métodos de diagnosis de fallos, principalmente aquellos importados del campo de “Aprendizaje Automático”. Estas técnicas de aprendizaje han resultado ser capaces de detectar y diagnosticar no sólo los fallos modelados o “aprendidos”, sino también nuevos fallos no incluidos en los modelos de diagnosis. Aunado a esto, algunas técnicas de mantenimiento basadas en riesgo (RBM) que son ampliamente usadas en la industria petroquímica, son también propuestas para su aplicación en el resto de sectores industriales como parte del mantenimiento preventivo. En conclusión, se propone implementar en un futuro no lejano un programa general de seguridad de planta que incluya el sistema de detección y diagnosis de fallos propuesto junto con un adecuado programa de mantenimiento preventivo. Desglosando el contenido de la tesis, el capítulo uno presenta una introducción general al tema de esta tesis, así como también la motivación generada para su desarrollo y el alcance delimitado. El capítulo dos expone el estado del arte de las áreas relacionadas al tema de tesis. De esta forma, los métodos de detección y diagnosis de fallos encontrados en la literatura son examinados en este capítulo. Asimismo, se propone una taxonomía de los métodos de diagnosis que unifica las clasificaciones propuestas en el área de Inteligencia Artificial y de Ingeniería de procesos. En consecuencia, se examina también la evaluación del performance de los métodos de diagnosis en la literatura. Además, en este capítulo se revisa y reporta el estado del arte correspondiente al “Análisis de Riesgos” y a la “Gestión del Mantenimiento” como técnicas complementarias para la toma de medidas correctoras y preventivas. Por último se abordan los casos de estudio considerados como puntos de referencia en el campo de investigación para la aplicación del sistema propuesto. La tercera parte incluye el capítulo siete, el cual constituye el corazón de la tesis. En este capítulo se presenta el esquema o sistema general de diagnosis de fallos propuesto. El sistema es dividido en tres partes: construcción de los modelos de diagnosis, validación de los modelos y aplicación on-line. Además incluye un modulo de detección de fallos previo a la diagnosis y una metodología de detección de anomalías para la detección de nuevos fallos. Por último, de este sistema se desglosan varias metodologías para procesos continuos y por lote. La cuarta parte de esta tesis presenta la validación de las metodologías propuestas. Específicamente, el capítulo ocho presenta la validación de las metodologías propuestas para su aplicación en procesos continuos y el capítulo nueve presenta la validación de las metodologías correspondientes a los procesos por lote. El capítulo diez valida la metodología de detección de anomalías en procesos por lote reales. Primero es aplicada a un intercambiador de calor escala laboratorio y después su aplicación es escalada a un proceso Foto-Fenton de planta piloto, lo cual corrobora el potencial y éxito de la metodología en la práctica real. Finalmente, la quinta parte de esta tesis, compuesta por el capítulo once, es dedicada a presentar y reafirmar las conclusiones finales y las principales contribuciones de la tesis. Además, se plantean las líneas de investigación futuras y se lista el trabajo desarrollado y presentado durante el periodo de investigación.
Fu, Qiang. "A generalization of the minimum classification error (MCE) training method for speech recognition and detection." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22705.
Повний текст джерелаGorecki, Christophe. "Classification par échantillonnage de la densité spectrale d'énergie : Application à l'étude statistique des surfaces et à l'analyse de particules." Besançon, 1989. http://www.theses.fr/1989BESA2015.
Повний текст джерелаDimara, Euthalie. "L'agriculture grecque : une étude chronologique et régionale par l'analyse des correspondances et la classification automatique." Paris 6, 1988. http://www.theses.fr/1988PA066199.
Повний текст джерелаSastre, Jurado Carlos. "Exploitation du signal pénétrométrique pour l'aide à l'obtention d'un modèle de terrain." Thesis, Université Clermont Auvergne (2017-2020), 2018. http://www.theses.fr/2018CLFAC003/document.
Повний текст джерелаThis research focuses on the site characterization of shallow soils using the dynamic cone penetrometer Panda® which uses variable energy. The main purpose is to study and propose several techniques as part of an overall method in order to obtain a ground model through a geotechnical campaign based on the Panda test.This work is divided into four parts, each of them it is focused on a specific topic :first of all, we introduce the main site characterization techniques, including the dynamic penetrometer Panda. Then, we present a brief overview of the geotechnical model and the mathematical methods for the characterization of uncertainties in soil properties;the second part deals with the automatic identification of physical homogeneous soil units based on penetration's mechanical response of the soil using the Panda test. Following a study about the soil layers identification based only on expert's judgment, we have proposed statistical moving window procedures for an objective assessment. The application of these statistical methods have been studied for the laboratory and in situ Panda test;the third part focuses on the automatic classification of the penetrations curves in the homogeneous soil units identified using the statistical techniques proposed in part II. An automatic methodology to predict the soil grading from the dynamic cone resistance using artificial neural networks has been proposed. The framework has been studied for two different research problems: the classification of natural soils and the classification of several crushed aggregate-bentonite mixtures;finally, the last chapter was devoted to model the spatial variability of the dynamic cone resistance qd based on random field theory and geostatistics. In order to reduce uncertainty in the field where Panda measurements are carried out, we have proposed the use of conditional simulation in a three dimensional space. This approach has been applied and studied to a real site investigation carried out in an alluvial mediterranean deltaic environment in Spain. Complementary studies in order to improve the proposed framework have been explored based on another geotechnical campaign conducted on a second experimental site in France
Wei, Yi. "Statistical methods on automatic aircraft recognition in aerial images." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248947.
Повний текст джерелаKim, Heeyoung. "Statistical methods for function estimation and classification." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44806.
Повний текст джерелаSun, Felice (Felice Tzu-yun) 1976. "Integrating statistical and knowledge-based methods for automatic phonemic segmentation." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80127.
Повний текст джерелаSezgin, Ozge. "Statistical Methods In Credit Rating." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607625/index.pdf.
Повний текст джерелаTowey, David John. "SPECT imaging and automatic classification methods in movement disorders." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11182.
Повний текст джерелаKolesov, Ivan A. "Statistical methods for coupling expert knowledge and automatic image segmentation and registration." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47739.
Повний текст джерелаRandolph, Tami Rochele. "Image compression and classification using nonlinear filter banks." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13439.
Повний текст джерелаMohamed, Ghada. "Text classification in the BNC using corpus and statistical methods." Thesis, Lancaster University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658020.
Повний текст джерелаWei, Xuelian. "Statistical methods in classification problems using gene expression / proteomic signatures." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1680042151&sid=2&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Повний текст джерелаArif, Omar. "Robust target localization and segmentation using statistical methods." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33882.
Повний текст джерелаTilley, Jason W. "A Comparison of Statistical Filtering Methods for Automatic Term Extraction for Domain Analysis." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/30818.
Повний текст джерелаMaster of Science
Anderson, Sarah G. "Statistical Methods for Biological and Relational Data." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365441350.
Повний текст джерелаSouthworth, Robin. "Classification of spatial data using neural networks and other statistical methods." Thesis, University of Leeds, 1997. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.679849.
Повний текст джерелаDambreville, Samuel. "Statistical and geometric methods for shape-driven segmentation and tracking." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22707.
Повний текст джерелаCommittee Chair: Allen Tannenbaum; Committee Member: Anthony Yezzi; Committee Member: Marc Niethammer; Committee Member: Patricio Vela; Committee Member: Yucel Altunbasak.
Fürst, Elmar Wilhelm, Peter Oberhofer, Christian Vogelauer, Rudolf Bauer, and David Martin Herold. "Innovative methods in European road freight transport statistics: A pilot study." Tayler and Francis, 2019. http://dx.doi.org/10.1080/09720510.2019.
Повний текст джерелаWu, Jian, and 武健. "Discriminative speaker adaptation and environmental robustness in automatic speech recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31246138.
Повний текст джерелаDelmege, James W. "CLASS : a study of methods for coarse phonetic classification /." Online version of thesis, 1988. http://hdl.handle.net/1850/10449.
Повний текст джерелаTansel, Icten. "Differentiation And Classification Of Counterfeit And Real Coins By Applying Statistical Methods." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614417/index.pdf.
Повний текст джерелаten M.Sc, Archaeometry Graduate Program Supervisor : Assist. Prof. Dr. Zeynep Isil Kalaylioglu Co-Supervisor : Prof. Dr. Sahinde Demirci June 2012, 105 pages In this study, forty coins which were obtained from Museum of Anatolian Civilizations (MAC) in Ankara were investigated. Some of those coins were real (twenty two coins) and the remaining ones (eighteen coins) were fake coins. Forty coins were Greek coins which were dated back to middle of the fifth century BCE and reign of Alexander the Great (323 &ndash
336 BCE). The major aims of this study can be summarized as follow
Leung, Yuk-yee, and 梁玉儀. "An integrated framework for feature selection and classification in microarray data analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43278632.
Повний текст джерела黃伯光 and Pak-kwong Wong. "Statistical language models for Chinese recognition: speech and character." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31239456.
Повний текст джерелаMcCormick, Neil Howie. "Bayesian methods for automatic segmentation and classification of SLO and SONAR data." Thesis, Heriot-Watt University, 2001. http://hdl.handle.net/10399/452.
Повний текст джерелаLamont, Morné Michael Connell. "Binary classification trees : a comparison with popular classification methods in statistics using different software." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52718.
Повний текст джерелаENGLISH ABSTRACT: Consider a data set with a categorical response variable and a set of explanatory variables. The response variable can have two or more categories and the explanatory variables can be numerical or categorical. This is a typical setup for a classification analysis, where we want to model the response based on the explanatory variables. Traditional statistical methods have been developed under certain assumptions such as: the explanatory variables are numeric only and! or the data follow a multivariate normal distribution. hl practice such assumptions are not always met. Different research fields generate data that have a mixed structure (categorical and numeric) and researchers are often interested using all these data in the analysis. hl recent years robust methods such as classification trees have become the substitute for traditional statistical methods when the above assumptions are violated. Classification trees are not only an effective classification method, but offer many other advantages. The aim of this thesis is to highlight the advantages of classification trees. hl the chapters that follow, the theory of and further developments on classification trees are discussed. This forms the foundation for the CART software which is discussed in Chapter 5, as well as other software in which classification tree modeling is possible. We will compare classification trees to parametric-, kernel- and k-nearest-neighbour discriminant analyses. A neural network is also compared to classification trees and finally we draw some conclusions on classification trees and its comparisons with other methods.
AFRIKAANSE OPSOMMING: Beskou 'n datastel met 'n kategoriese respons veranderlike en 'n stel verklarende veranderlikes. Die respons veranderlike kan twee of meer kategorieë hê en die verklarende veranderlikes kan numeries of kategories wees. Hierdie is 'n tipiese opset vir 'n klassifikasie analise, waar ons die respons wil modelleer deur gebruik te maak van die verklarende veranderlikes. Tradisionele statistiese metodes is ontwikkelonder sekere aannames soos: die verklarende veranderlikes is slegs numeries en! of dat die data 'n meerveranderlike normaal verdeling het. In die praktyk word daar nie altyd voldoen aan hierdie aannames nie. Verskillende navorsingsvelde genereer data wat 'n gemengde struktuur het (kategories en numeries) en navorsers wil soms al hierdie data gebruik in die analise. In die afgelope jare het robuuste metodes soos klassifikasie bome die alternatief geword vir tradisionele statistiese metodes as daar nie aan bogenoemde aannames voldoen word nie. Klassifikasie bome is nie net 'n effektiewe klassifikasie metode nie, maar bied baie meer voordele. Die doel van hierdie werkstuk is om die voordele van klassifikasie bome uit te wys. In die hoofstukke wat volg word die teorie en verdere ontwikkelinge van klassifikasie bome bespreek. Hierdie vorm die fondament vir die CART sagteware wat bespreek word in Hoofstuk 5, asook ander sagteware waarin klassifikasie boom modelering moontlik is. Ons sal klassifikasie bome vergelyk met parametriese-, "kernel"- en "k-nearest-neighbour" diskriminant analise. 'n Neurale netwerk word ook vergelyk met klassifikasie bome en ten slote word daar gevolgtrekkings gemaak oor klassifikasie bome en hoe dit vergelyk met ander metodes.
Ravindran, Sourabh. "Physiologically Motivated Methods For Audio Pattern Classification." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14066.
Повний текст джерелаLiu, Lihong. "Molecular phylogeny, classification, evolution and detection of pestiviruses /." Uppsala : Dept. of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, 2009. http://epsilon.slu.se/20098.pdf.
Повний текст джерелаZhong, Xiao. "A study of several statistical methods for classification with application to microbial source tracking." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0430104-155106/.
Повний текст джерелаKeywords: classification; k-nearest-neighbor (k-n-n); neural networks; linear discriminant analysis (LDA); support vector machines; microbial source tracking (MST); quadratic discriminant analysis (QDA); logistic regression. Includes bibliographical references (p. 59-61).
Lavrik, Ilya A. "Novel Wavelet-Based Statistical Methods with Applications in Classification, Shrinkage, and Nano-Scale Image Analysis." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/10424.
Повний текст джерелаALQADAH, HATIM FAROUQ. "OPTIMIZED TIME-FREQUENCY CLASSIFICATION METHODS FOR INTELLIGENT AUTOMATIC JETTISONING OF HELMET-MOUNTED DISPLAY SYSTEMS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1185838368.
Повний текст джерелаBodin, Camilla. "Automatic Flight Maneuver Identification Using Machine Learning Methods." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165844.
Повний текст джерелаLavrik, Ilya A. "Novel wavelet-based statistical methods with applications in classification, shrinkage, and nano-scale image analysis." Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-11162005-131744/.
Повний текст джерелаHuo, Xiaoming, Committee Member ; Heil, Chris, Committee Member ; Wang, Yang, Committee Member ; Hayter, Anthony, Committee Member ; Vidakovic, Brani, Committee Chair.
Moser, Paolo 1985, Alexander Christian 1959 Vibrans, Ronald McRoberts, and Universidade Regional de Blumenau Programa de Pós-Graduação em Engenharia Ambiental. "Statistical and computational methods of forest attribute estimation and classification based on remotely sensed data." reponame:Biblioteca Digital de Teses e Dissertações FURB, 2018. http://www.bc.furb.br/docs/TE/2018/364705_1_1.pdf.
Повний текст джерелаCoorientador: Ronald McRoberts.
Tese (Doutorado em Engenharia Ambiental) - Programa de Pós-Graduação em Engenharia Ambiental, Centro de Ciências Tecnológicas, Universidade Regional de Blumenau, Blumenau.
Gurol, Selime. "Statistical Learning And Optimization Methods For Improving The Efficiency In Landscape Image Clustering And Classification Problems." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606595/index.pdf.
Повний текст джерелаJianguo, Li. "Hybrid Methods for Acquisition of Lexical Information: the Case for Verbs." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1228259857.
Повний текст джерелаChan, Oscar. "Prosodic features for a maximum entropy language model." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0244.
Повний текст джерелаChalla, Akkireddy. "Automatic Handwritten Digit Recognition On Document Images Using Machine Learning Methods." Thesis, Blekinge Tekniska Högskola, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17656.
Повний текст джерелаAzarbarzin, Ali. "Snoring sounds analysis: automatic detection, higher order statistics, and its application for sleep apnea diagnosis." IEEE, 2011. http://hdl.handle.net/1993/9593.
Повний текст джерелаBrookey, Carla M. "Application of Machine Learning and Statistical Learning Methods for Prediction in a Large-Scale Vegetation Map." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6962.
Повний текст джерелаGerber, Egardt. "The use of classification methods for gross error detection in process data." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85856.
Повний текст джерелаENGLISH ABSTRACT: All process measurements contain some element of error. Typically, a distinction is made between random errors, with zero expected value, and gross errors with non-zero magnitude. Data Reconciliation (DR) and Gross Error Detection (GED) comprise a collection of techniques designed to attenuate measurement errors in process data in order to reduce the effect of the errors on subsequent use of the data. DR proceeds by finding the optimum adjustments so that reconciled measurement data satisfy imposed process constraints, such as material and energy balances. The DR solution is optimal under the assumed statistical random error model, typically Gaussian with zero mean and known covariance. The presence of outliers and gross errors in the measurements or imposed process constraints invalidates the assumptions underlying DR, so that the DR solution may become biased. GED is required to detect, identify and remove or otherwise compensate for the gross errors. Typically GED relies on formal hypothesis testing of constraint residuals or measurement adjustment-based statistics derived from the assumed random error statistical model. Classification methodologies are methods by which observations are classified as belonging to one of several possible groups. For the GED problem, artificial neural networks (ANN’s) have been applied historically to resolve the classification of a data set as either containing or not containing a gross error. The hypothesis investigated in this thesis is that classification methodologies, specifically classification trees (CT) and linear or quadratic classification functions (LCF, QCF), may provide an alternative to the classical GED techniques. This hypothesis is tested via the modelling of a simple steady-state process unit with associated simulated process measurements. DR is performed on the simulated process measurements in order to satisfy one linear and two nonlinear material conservation constraints. Selected features from the DR procedure and process constraints are incorporated into two separate input vectors for classifier construction. The performance of the classification methodologies developed on each input vector is compared with the classical measurement test in order to address the posed hypothesis. General trends in the results are as follows: - The power to detect and/or identify a gross error is a strong function of the gross error magnitude as well as location for all the classification methodologies as well as the measurement test. - For some locations there exist large differences between the power to detect a gross error and the power to identify it correctly. This is consistent over all the classifiers and their associated measurement tests, and indicates significant smearing of gross errors. - In general, the classification methodologies have higher power for equivalent type I error than the measurement test. - The measurement test is superior for small magnitude gross errors, and for specific locations, depending on which classification methodology it is compared with. There is significant scope to extend the work to more complex processes and constraints, including dynamic processes with multiple gross errors in the system. Further investigation into the optimal selection of input vector elements for the classification methodologies is also required.
AFRIKAANSE OPSOMMING: Alle prosesmetings bevat ʼn sekere mate van metingsfoute. Die fout-element van ʼn prosesmeting word dikwels uitgedruk as bestaande uit ʼn ewekansige fout met nul verwagte waarde, asook ʼn nie-ewekansige fout met ʼn beduidende grootte. Data Rekonsiliasie (DR) en Fout Opsporing (FO) is ʼn versameling van tegnieke met die doelwit om die effek van sulke foute in prosesdata op die daaropvolgende aanwending van die data te verminder. DR word uitgevoer deur die optimale veranderinge aan die oorspronklike prosesmetings aan te bring sodat die aangepaste metings sekere prosesmodelle gehoorsaam, tipies massa- en energie-balanse. Die DR-oplossing is optimaal, mits die statistiese aannames rakende die ewekansige fout-element in die prosesdata geldig is. Dit word tipies aanvaar dat die fout-element normaal verdeel is, met nul verwagte waarde, en ʼn gegewe kovariansie matriks. Wanneer nie-ewekansige foute in die data teenwoordig is, kan die resultate van DR sydig wees. FO is daarom nodig om nie-ewekansige foute te vind (Deteksie) en te identifiseer (Identifikasie). FO maak gewoonlik staat op die statistiese eienskappe van die meting aanpassings wat gemaak word deur die DR prosedure, of die afwykingsverskil van die model vergelykings, om formele hipoteses rakende die teenwoordigheid van nie-ewekansige foute te toets. Klassifikasie tegnieke word gebruik om die klasverwantskap van observasies te bepaal. Rakende die FO probleem, is sintetiese neurale netwerke (SNN) histories aangewend om die Deteksie en Identifikasie probleme op te los. Die hipotese van hierdie tesis is dat klassifikasie tegnieke, spesifiek klassifikasiebome (CT) en lineêre asook kwadratiese klassifikasie funksies (LCF en QCF), suksesvol aangewend kan word om die FO probleem op te los. Die hipotese word ondersoek deur middel van ʼn simulasie rondom ʼn eenvoudige gestadigde toestand proses-eenheid wat aan een lineêre en twee nie-lineêre vergelykings onderhewig is. Kunsmatige prosesmetings word geskep met behulp van lukrake syfers sodat die foutkomponent van elke prosesmeting bekend is. DR word toegepas op die kunsmatige data, en die DR resultate word gebruik om twee verskillende insetvektore vir die klassifikasie tegnieke te skep. Die prestasie van die klassifikasie metodes word vergelyk met die metingstoets van klassieke FO ten einde die gestelde hipotese te beantwoord. Die onderliggende tendense in die resultate is soos volg: - Die vermoë om ‘n nie-ewekansige fout op te spoor en te identifiseer is sterk afhanklik van die grootte asook die ligging van die fout vir al die klassifikasie tegnieke sowel as die metingstoets. - Vir sekere liggings van die nie-ewekansige fout is daar ‘n groot verskil tussen die vermoë om die fout op te spoor, en die vermoë om die fout te identifiseer, wat dui op smering van die fout. Al die klassifikasie tegnieke asook die metingstoets baar hierdie eienskap. - Oor die algemeen toon die klassifikasie metodes groter sukses as die metingstoets. - Die metingstoets is meer suksesvol vir relatief klein nie-ewekansige foute, asook vir sekere liggings van die nie-ewekansige fout, afhangende van die klassifikasie tegniek ter sprake. Daar is verskeie maniere om die bestek van hierdie ondersoek uit te brei. Meer komplekse, niegestadigde prosesse met sterk nie-lineêre prosesmodelle en meervuldige nie-ewekansige foute kan ondersoek word. Die moontlikheid bestaan ook om die prestasie van klassifikasie metodes te verbeter deur die gepaste keuse van insetvektor elemente.
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Повний текст джерелаWu, Tsung-Lin. "Classification models for disease diagnosis and outcome analysis." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44918.
Повний текст джерелаBankefors, Johan. "Structural classification of Quillaja saponins by electrospray ionisation ion trap multiple-stage mass spectrometry in combination with multivariate analysis /." Uppsala : Department of Chemistry, Swedish University of Agricultural Sciences, 2006. http://epsilon.slu.se/10284550.pdf.
Повний текст джерелаFairley, Jacqueline Antoinette. "Statistical modeling of the human sleep process via physiological recordings." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33912.
Повний текст джерелаTiwari, Ayush. "Comparison of Statistical Signal Processing and Machine Learning Algorithms as Applied to Cognitive Radios." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1533218513862248.
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