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Dissertations / Theses on the topic 'Artificail neural networks'

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Published: 4 June 2021

Last updated: 18 February 2022

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1

Moreno, Barón Laura. "Avances en lenguas electrónicas voltamperométricas y su aplicación en la multideterminación de compuestos presentes en muestras complejas." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/402232.

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La presente tesis doctoral he tenido como objetivo la aplicación de lenguas electrónicas en dos áreas concretas, la determinación de aminoácidos oxidables en matrices complejas (muestras de pienso animal), y la determinación de metales pesados en muestras de agua que contiene como interferentes otros metales a nivel de traza. Para ambos casos se ha establecido un protocolo de trabajo que ha consistido en la caracterización de sistema de medida mediante el estudio de la respuesta amperométrica y posterior el análisis de la señal a través de técnicas clásicas, (quimiometría PLS en el caso de

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2

Chambers, Mark Andrew. "Queuing network construction using artificial neural networks /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193665234291.

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3

Ariza, Zambrano William Camilo 1989. "Controle ativo de vibrações usando redes neurais artificiais : Active vibration control using artificial neural networks." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264927.

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Orientador: Alberto Luiz Serpa<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-23T23:04:02Z (GMT). No. of bitstreams: 1 ArizaZambrano_WilliamCamilo_M.pdf: 5789145 bytes, checksum: 151f5e3ef1780d5448a7073b85b4715f (MD5) Previous issue date: 2013<br>Resumo: Este trabalho tem como objetivo principal o estudo de um método de controle baseado no uso de redes neurais artificiais aplicado ao problema de controle de vibrações em estruturas flexíveis. Este trabalho centra-se no estudo do esquema de controle inverso

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4

BRUCE, WILLIAM, and OTTER EDVIN VON. "Artificial Neural Network Autonomous Vehicle : Artificial Neural Network controlled vehicle." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191192.

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This thesis aims to explain how a Artificial Neural Network algorithm could be used as means of control for a Autonomous Vehicle. It describes the theory behind the neural network and Autonomous Vehicles, and how a prototype with a camera as its only input can be designed to test and evaluate the algorithms capabilites, and also drive using it. The thesis will show that the Artificial Neural Network can, with a image resolution of 100 × 100 and a training set with 900 images, makes decisions with a 0.78 confidence level.<br>Denna rapport har som mal att beskriva hur en Artificiellt Neuronnatve

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5

Menneer, Tamaryn Stable Ia. "Quantum artificial neural networks." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286530.

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6

Dai, Jing. "Reservoir-computing-based, biologically inspired artificial neural networks and their applications in power systems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47646.

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Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural netwo

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7

Varoonchotikul, Pichaid. "Flood forecasting using artificial neural networks /." Lisse : Balkema, 2003. http://www.e-streams.com/es0704/es0704_3168.html.

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8

Orr, Ewan. "Evolving Turing's Artificial Neural Networks." Thesis, University of Canterbury. Department of Physics and Astronomy, 2010. http://hdl.handle.net/10092/4620.

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Our project uses ideas first presented by Alan Turing. Turing's immense contribution to mathematics and computer science is widely known, but his pioneering work in artificial intelligence is relatively unknown. In the late 1940s Turing introduced discrete Boolean artificial neural networks and, it has been argued that, he suggested that these networks be trained via evolutionary algorithms. Both artificial neural networks and evolutionary algorithms are active fields of research. Turing's networks are very basic yet capable of complex tasks such as processing sequential input; consequently, t

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9

Xu, Shuxiang. "Neuron-adaptive neural network models and applications /." [Campbelltown, N.S.W. : The Author], 1999. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030702.085320/index.html.

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10

Fraticelli, Chiara. "Λc reconstruction with artificial neural networks". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19985/.

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Il rivelatore ALICE studia collisioni di ioni pesanti ultrarelativistici in modo da creare e di conseguenza studiare lo stato della materia chiamato plasma di quark e gluoni. Questo obiettivo risulta difficoltoso data la sua vita breve, quindi facciamo riferimento a misure indirette per la prova della sua esistenza. In questa tesi abbiamo sfruttato tecniche di machine learning per studiare il decadimento del barione charmato Λc per dedurre alcune sue proprietà. In particolare abbiamo usato il metodo delle reti neurali per ricavare tutte le informazioni possibili con la tecninca di un'analisi m

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Prasad, Jayan Ganesh Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "Financial forecasting using artificial neural networks." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2008. http://handle.unsw.edu.au/1959.4/38700.

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Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network predict

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12

Ng, Roger K. W. "Rapid prototyping of artificial neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23440.pdf.

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13

Hook, Jaroslav. "Are artificial neural networks learning machines?" Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ38651.pdf.

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14

Coulibaly, Paulin. "Artificial neural networks for hydrological forecasting." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0028/NQ52236.pdf.

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15

Baker, Thomas Edward. "Implementation limits for artificial neural networks." Full text open access at:, 1990. http://content.ohsu.edu/u?/etd,268.

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16

Watkins, Bruce E. "Data compression using artificial neural networks." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/25801.

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Approved for public release; distribution is unlimited<br>This thesis investigates the application of artificial neural networks for the compression of image data. An algorithm is developed using the competitive learning paradigm which takes advantage of the parallel processing and classification capability of neural networks to produce an efficient implementation of vector quantization. Multi-Stage, tree searched, and classification vector quantization codebook design techniques are adapted to the neural network design to reduce the computational cost and hardware requirements. The resu

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17

Gulliford, Sarah Louise. "Artificial neural networks applied to radiotherapy." Thesis, Institute of Cancer Research (University Of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404474.

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18

Garvin, Alan David Morris. "Self-structuring of artificial neural networks." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307948.

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19

Miranda, Trujillo Luis Carlos. "Artificial Neural Networks in Greenhouse Modelling." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19354.

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Moderne Präzisionsgartenbaulicheproduktion schließt hoch technifizierte Gewächshäuser, deren Einsatz in großem Maße von der Qualität der Sensorik- und Regelungstechnik abhängt, mit ein. Zu den Regelungsstrategien gehören unter anderem Methoden der Künstlichen Intelligenz, wie z.B. Künstliche Neuronale Netze (KNN, aus dem Englischen). Die vorliegende Arbeit befasst sich mit der Eignung KNN-basierter Modelle als Bauelemente von Klimaregelungstrategien in Gewächshäusern. Es werden zwei Modelle vorgestellt: Ein Modell zur kurzzeitigen Voraussage des Gewächshausklimas (Lufttemperatur und relative

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20

Zhong, Xiaolin. "Robot calibration using artificial neural networks." Thesis, Edinburgh Napier University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295387.

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21

Rodríguez, Villegas Antoni. "Polyp segmentation using artificial neural networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-98001.

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Colorectal cancer is the second cause of cancer death in the world. Aiming to early detect and prevent this type of cancer, clinicians perform screenings through the colon searching for polyps (colorectal cancer precursor lesions).If found, these lesions are susceptible of being removed in order to further ana-lyze their malignancy degree. Automatic polyp segmentation is of primary impor-tance when it comes to computer-aided medical diagnosis using images obtained in colonoscopy screenings. These results allow for more precise medical diagnosis which can lead to earlier detection.This project

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22

Townsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbol

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23

Lind, Benjamin. "Artificial Neural Networks for Image Improvement." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-137661.

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After a digital photo has been taken by a camera, it can be manipulated to be more appealing. Two ways of doing that are to reduce noise and to increase the saturation. With time and skills in an image manipulating program, this is usually done by hand. In this thesis, automatic image improvement based on artificial neural networks is explored and evaluated qualitatively and quantitatively. A new approach, which builds on an existing method for colorizing gray scale images is presented and its performance compared both to simpler methods and the state of the art in image denoising. Saturation

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24

Millevik, Daniel, and Michael Wang. "Stock Forecasting Using Artificial Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166455.

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This paper studies the potential of artificial neural networks (ANNs) in stock forecasting. It also investigates how the number of neurons in the network, as well as the distribution of the training data into training, validation and testing sets, affect the accuracy of the network. By using MATLAB and its Neural Network Toolbox tests were carried out with a two-layer feedforward neural network (FFNN). These are carried out by collecting five years of historical data from the Dow Jones Industrial Average (DJIA) stock index, which is then used for training the network. Finally, retraining the n

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25

Bolt, George Ravuama. "Fault tolerance in artificial neural networks : are neural networks inherently fault tolerant?" Thesis, University of York, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317683.

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26

Kadirkamanathan, Visakan. "Sequential learning in artifical neural networks." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334136.

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27

Cottens, Pablo Eduardo Pereira de Araujo. "Development of an artificial neural network architecture using programmable logic." Universidade do Vale do Rio dos Sinos, 2016. http://www.repositorio.jesuita.org.br/handle/UNISINOS/5411.

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Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-06-29T14:42:16Z No. of bitstreams: 1 Pablo Eduardo Pereira de Araujo Cottens_.pdf: 1315690 bytes, checksum: 78ac4ce471c2b51e826c7523a01711bd (MD5)<br>Made available in DSpace on 2016-06-29T14:42:16Z (GMT). No. of bitstreams: 1 Pablo Eduardo Pereira de Araujo Cottens_.pdf: 1315690 bytes, checksum: 78ac4ce471c2b51e826c7523a01711bd (MD5) Previous issue date: 2016-03-07<br>Nenhuma<br>Normalmente Redes Neurais Artificiais (RNAs) necessitam estações de trabalho para o seu processamento, por causa da complexidade do siste

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Nagaoka, Maria Eiko [UNESP]. "Uma abordagem baseada em redes neurais artificiais para a estimação de densidade de solo." Universidade Estadual Paulista (UNESP), 2003. http://hdl.handle.net/11449/101853.

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Made available in DSpace on 2014-06-11T19:31:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2003Bitstream added on 2014-06-13T20:22:35Z : No. of bitstreams: 1 nagaoka_me_dr_botfca.pdf: 501587 bytes, checksum: a5d05cfa41f21298548d31b5d95dc6b1 (MD5)<br>Este trabalho apresenta a aplicação de um sistema inteligente utilizando redes neurais artificiais para estimar valores de densidade do solo, a partir de parâmetros referentes à resistência do solo à penetração. Foram considerados solos preparados e não preparados, os não preparados foram os seguintes : teor de argila menor que 30 % (solo

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29

Nordberg, Marcus, and Simon Karlsson. "Stock market index prediction using artificial neural networks trained on foreign markets : And how they compare to a domestic artificial neural network." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166774.

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In this report, the location dependency of stock predicting articial neural networks(ANNs) is investigated. Five ANNs of the type feed forward network are trained on vedierent stock market indices (Denmark, Germany, Japan, Sweden and USA) and thencross-tested on the other markets to examine what impact this has on the predictionperformance. It is found that the ANNs perform similarly, regardless of the marketit has been trained on. The conclusion of the study is that ANNs trained on foreignmarkets perform comparably to a domesticly trained ANN. While the results appear tobe promising, more res

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Adamu, Abdullahi S. "An empirical study towards efficient learning in artificial neural networks by neuronal diversity." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33799/.

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Artificial Neural Networks (ANN) are biologically inspired algorithms, and it is natural that it continues to inspire research in artificial neural networks. From the recent breakthrough of deep learning to the wake-sleep training routine, all have a common source of drawing inspiration: biology. The transfer functions of artificial neural networks play the important role of forming decision boundaries necessary for learning. However, there has been relatively little research on transfer function optimization compared to other aspects of neural network optimization. In this work, neuronal dive

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Guler, Altug. "Seismic Vulnerability Assessment Using Artificial Neural Networks." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606067/index.pdf.

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In this study, an alternative seismic vulnerability assessment model is developed. For this purpose, one of the most popular artificial intelligence techniques, Artificial Neural Network (ANN), is used. Many ANN models are generated using 4 different network training functions, 1 to 50 hidden neurons and combination of structural parameters like number of stories, normalized redundancy scores, overhang ratios, soft story indices, normalized total column areas, normalized total wall areas are used to achieve the best assessment performance. Duzce database is used throughout the thesis for tra

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32

Frøyen, Even Bruvik. "Exploring Learning in Evolutionary Artificial Neural Networks." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-15689.

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Evolutionary artificial neural networks can adapt to new circumstances, and handle slight changes without catastrophic failure. However, under constantly changing circumstances, resulting in unpredictable grounds for evaluating success, the lack of memory of previous adaptations are a limiting factor. While further evolution can allow adaptations to new changes, the same is required for a return to a previous environment. To reduce the need for further evolution to deal with previously seen problems, this thesis looks at an approach to encourage previous knowledge to be retained across generat

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33

Lindefelt, Lisa. "Predicting gene expression using artificial neural networks." Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-707.

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<p>Today one of the greatest aims within the area of bioinformatics is to gain a complete understanding of the functionality of genes and the systems behind gene regulation. Regulatory relationships among genes seem to be of a complex nature since transcriptional control is the result of complex networks interpreting a variety of inputs. It is therefore essential to develop analytical tools detecting complex genetic relationships.</p><p>This project examines the possibility of the data mining technique artificial neural network (ANN) detecting regulatory relationships between genes. As an init

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34

Cavaco, Philip. "Artificial Grammar Recognition Using Spiking Neural Networks." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-5875.

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<p>This thesis explores the feasibility of Artificial Grammar (AG) recognition using spiking neural networks. A biologically inspired minicolumn model is designed as the base computational unit. Two network topographies are defined with different ideologies. Both networks consists of minicolumn models, referred to as nodes, connected with excitatory and inhibitory connections. The first network contains nodes for every bigram and trigram producible by the grammar’s finite state machine (FSM). The second network has only nodes required to identify unique internal states of the FSM. The networks

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35

Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all s

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Turner, Joe. "Application of artificial neural networks in pharmacokinetics /." Connect to full text, 2003. http://setis.library.usyd.edu.au/adt/public_html/adt-NU/public/adt-NU20031007.090937/index.html.

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37

Gou, Zhenkun. "Canonical correlation analysis and artificial neural networks." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269409.

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38

Hasan, Usama. "Artificial neural networks for voltage collapse monitoring." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286623.

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Han, Ying. "Analysing time series using artificial neural networks." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398318.

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Kendall, Gary David. "Non-linear modelling through artificial neural networks." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300608.

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Porter, Nicholas David. "Facial feature processing using artificial neural networks." Thesis, University of Warwick, 1998. http://wrap.warwick.ac.uk/59539/.

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Describing a human face is a natural ability used in eveyday life. To the police, a witness description of a suspect is key evidence in the identification of the suspect. However, the process of examining "mug shots" to find a match to the description is tedious and often unfruitful. If a description could be stored with each photograph and used as a searchable index, this would provide a much more effective means of using "mug shots" for identification purposes. A set of descriptive measures have been defined by Shepherd [73] which seek to describe faces in a manner that may be used for just

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Muthuraman, Sethuraman. "The evolution of modular artificial neural networks." Thesis, Robert Gordon University, 2005. http://hdl.handle.net/10059/284.

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This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming and Genetic Programming; however, these often fail in the evolution of complex systems, particularly when such systems involve multi-domain sensory information which interacts in complex ways with system outputs. The aim in this work is to produce an evolutionary method that allows the structure of the network to evolve from simple to complex as it interacts with a

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Chen, Jian-Rong. "Theory and applications of artificial neural networks." Thesis, Durham University, 1991. http://etheses.dur.ac.uk/6240/.

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In this thesis some fundamental theoretical problems about artificial neural networks and their application in communication and control systems are discussed. We consider the convergence properties of the Back-Propagation algorithm which is widely used for training of artificial neural networks, and two stepsize variation techniques are proposed to accelerate convergence. Simulation results demonstrate significant improvement over conventional Back-Propagation algorithms. We also discuss the relationship between generalization performance of artificial neural networks and their structure and

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D'Souza, Winston Anthony. "Real-time applications of artificial neural networks." Thesis, University of Aberdeen, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445136.

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This research takes an innovative look at two distinct applications of Artificial Neural Networks (ANNs) concerning the manipulation of data within real-time systems. The first contribution of this research involves the filtering of errors associated with data emergent from Inertial Navigation Systems (INS) by adopting an ANN filter. This novel approach when compared to present day optimal estimation filter techniques for random data such as the Kalman Filter (KF) and its variants, offers a better estimated response without the need to mathematically model error. In addition to this advantag

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45

Zhu, Kangmin. "ECG feature recognition using artificial neural networks." Thesis, University of Essex, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316072.

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Kurd, Zeshan. "Artificial neural networks in safety-critical applications." Thesis, University of York, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428472.

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Rust, Alistair Gibson. "Developmental self-organisation in artificial neural networks." Thesis, University of Hertfordshire, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268042.

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48

Ferguson, Alistair. "Learning in RAM-based artificial neural networks." Thesis, University of Hertfordshire, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283866.

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49

Berry, Ian Michael. "Data classification using unsupervised artificial neural networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390079.

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Gavrilov, Alexander I. "Welding process engineering with artificial neural networks." Thesis, De Montfort University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420851.

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