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Tesi sul tema "Neural network"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

Xu, Shuxiang, University of Western Sydney, and of Informatics Science and Technology Faculty. "Neuron-adaptive neural network models and applications." THESIS_FIST_XXX_Xu_S.xml, 1999. http://handle.uws.edu.au:8081/1959.7/275.

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Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problems such as function approximation and data simulation. This thesis deals with Feed-forward Neural Networks (FNN's) with a new neuron activation function called Neuron-adaptive Activation Function (NAF), and Feed-forward Higher Order Neural Networks (HONN's) with this new neuron activation function. We have designed a new neural network model, the Neuron-Adaptive Neural Network (NANN), and mathematically proved that one NANN can approximate any piecewise continuous function to any desired accuracy

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2

Ellerbrock, Thomas M. "Multilayer neural networks learnability, network generation, and network simplification /." [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=958467897.

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3

Patterson, Raymond A. "Hybrid Neural networks and network design." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262707683.

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4

Khliobas. "NEURAL NETWORK." Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33752.

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5

Rastogi, Preeti. "Assessing Wireless Network Dependability Using Neural Networks." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1129134364.

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6

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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7

Dunn, Nathan A. "A Novel Neural Network Analysis Method Applied to Biological Neural Networks." Thesis, view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?did=1251892251&sid=2&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2006.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 122- 131). Also available for download via the World Wide Web; free to University of Oregon users.

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8

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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9

De, Jongh Albert. "Neural network ensembles." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50035.

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Thesis (MSc)--Stellenbosch University, 2004.<br>ENGLISH ABSTRACT: It is possible to improve on the accuracy of a single neural network by using an ensemble of diverse and accurate networks. This thesis explores diversity in ensembles and looks at the underlying theory and mechanisms employed to generate and combine ensemble members. Bagging and boosting are studied in detail and I explain their success in terms of well-known theoretical instruments. An empirical evaluation of their performance is conducted and I compare them to a single classifier and to each other in terms of accuracy

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10

Simmen, Martin Walter. "Neural network optimization." Thesis, University of Edinburgh, 1992. http://hdl.handle.net/1842/12942.

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Combinatorial optimization problems arise throughout science, industry, and commerce. The demonstration that analogue neural networks could, in principle, rapidly find near-optimal solutions to such problems - many of which appear computationally intractable - was important both for the novelty of the approach and because these networks are potentially implementable in parallel hardware. However, subsequent research, conducted largely on the travelling salesman problem, revealed problems regarding the original network's parameter sensitivity and tendency to give invalid states. Although this h

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11

Смаль, Богдан Віталійович. "Artificial Neural Network." Thesis, Київський національний університет технологій та дизайну, 2017. https://er.knutd.edu.ua/handle/123456789/7384.

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12

Post, David L. "Network Management: Assessing Internet Network-Element Fault Status Using Neural Networks." Ohio : Ohio University, 2008. http://www.ohiolink.edu/etd/view.cgi?ohiou1220632155.

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13

Xu, Shuxiang. "Neuron-adaptive neural network models and applications." Thesis, [Campbelltown, N.S.W. : The Author], 1999. http://handle.uws.edu.au:8081/1959.7/275.

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Abstract (sommario):

Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problems such as function approximation and data simulation. This thesis deals with Feed-forward Neural Networks (FNN's) with a new neuron activation function called Neuron-adaptive Activation Function (NAF), and Feed-forward Higher Order Neural Networks (HONN's) with this new neuron activation function. We have designed a new neural network model, the Neuron-Adaptive Neural Network (NANN), and mathematically proved that one NANN can approximate any piecewise continuous function to any desired accuracy

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14

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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15

Brande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.

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Abstract (sommario):

The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks. Effective routing algorithms use various techniques to determine the most

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16

Ayoub, Issa. "Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39337.

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Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology. Often, researchers describe affect using emotional dimensions such as arousal and valence. Valence refers to the spectrum of negative to positive emotions while arousal determines the level of excitement. Describing emotions through continuous dimensions (e.g. valence and arousal) allows us to encode subtle and complex affects as opposed to discrete emotions, such as the basic six emotions: happy, anger, fear, disgust, sad and n

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17

Roadknight, C. M. "Transparent neural network modelling." Thesis, Nottingham Trent University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314107.

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18

Kulakov, Anton. "Multiprocessing neural network simulator." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/348420/.

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Over the last few years tremendous progress has been made in neuroscience by employing simulation tools for investigating neural network behaviour. Many simulators have been created during last few decades, and their number and set of features continually grows due to persistent interest from groups of researchers and engineers. A simulation software that is able to simulate a large-scale neural network has been developed and presented in this work. Based on a highly abstract integrate-and-fire neuron model a clock-driven sequential simulator has been developed in C++. The created program is a

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19

Åström, Fredrik. "Neural Network on Compute Shader : Running and Training a Neural Network using GPGPU." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2036.

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In this thesis I look into how one can train and run an artificial neural network using Compute Shader and what kind of performance can be expected. An artificial neural network is a computational model that is inspired by biological neural networks, e.g. a brain. Finding what kind of performance can be expected was done by creating an implementation that uses Compute Shader and then compare it to the FANN library, i.e. a fast artificial neural network library written in C. The conclusion is that you can improve performance by training an artificial neural network on the compute shader as long

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20

Turk, Fethi. "Improvements To Neural Network Based Restoration In Optical Networks." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609491/index.pdf.

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Performance of neural network based restoration of optical networks is evaluated and a few possible improvements are proposed. Neural network based restoration is simulated with optical link capacities assigned by a new method. Two new improvement methods are developed to reduce the neural network size and the restoration time of severed optical connections. Cycle based restoration is suggested, which reduces the neural network structure by restoring the severed connections for each optical node, iteratively. Additionally, to reduce the restoration time, the necessary waiting time before the n

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21

Onyiagha, Chyke Godfrey. "Intelligent neural network access control of ATM network." Thesis, King's College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271321.

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22

Leija, Carlos Ivan. "An artificial neural network with reconfigurable interconnection network." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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23

Emery, Robin. "Neural network-on-chip : A connection-centric reconfigurable platform for biological neural network models." Thesis, University of Newcastle Upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.531749.

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24

McMichael, Lonny D. (Lonny Dean). "A Neural Network Configuration Compiler Based on the Adaptrode Neuronal Model." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc501018/.

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A useful compiler has been designed that takes a high level neural network specification and constructs a low level configuration file explicitly specifying all network parameters and connections. The neural network model for which this compiler was designed is the adaptrode neuronal model, and the configuration file created can be used by the Adnet simulation engine to perform network experiments. The specification language is very flexible and provides a general framework from which almost any network wiring configuration may be created. While the compiler was created for the specialized ada

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25

Pasca, Isabela Mona. "Neural network digital hardware implementation." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27902.

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This thesis presents a digital hardware implementation of an artificial neuron with learning ability using the QuartusII 5.1sp1 web edition software on Altera's University Program Development Board (UP2). The learning method implemented is neither backpropagation nor conjugate gradient, but the weight simultaneous perturbation. By combining this method with a pulse density system and using a Field Programmable Gate Array, an interesting artificial neuron hardware architecture is obtained. Finally, two applications of the neuron implementation are presented: an analog function and a digital fun

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26

Siqueira, Ines. "Neural network-based cost estimating." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0019/MQ47816.pdf.

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27

Bahr, Casey S. "Anne : another neural network emulator /." Full text open access at:, 1988. http://content.ohsu.edu/u?/etd,173.

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28

Guerin, Olivier Cedrick. ""Neural network" based process monitoring." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/16804.

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29

Littlewort, G. C. "Neural network analysis and simulation." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292677.

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30

Messom, Christopher H. "Engineering reliable neural network systems." Thesis, Loughborough University, 1992. https://dspace.lboro.ac.uk/2134/14137.

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This thesis presents a study of neural network representation and behaviour. The study places neural networks in the context of designing reliable systems. Several new results on network size and topology are presented. Knowledge based training of neural networks is examined. This is essential for designing reliable neural systems in which the subsymbolic reasoning processes are well defined. Sandwich nodes are introduced and studied as atomic knowledge elements in neural networks. Two new network architectures are introduced, the Loughborough Net and the Loughborough Control Net. These make u

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31

Ghafoori, Elyar. "Wavelet transform and neural network." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527935.

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<p> Automatic and accurate detection of Atrial Fibrillation (AF) from the noninvasive ECG signal is imperative in Electrocardiography. AF is mainly reflected in the ECG signal with the absence of P wave and/or irregular RR intervals. Signal processing tools can assess such detailed changes in the ECG, leading to an accurate diagnosis of AF. The proposed method relies on proper noise filtering, Stationary Wavelet Transform, and signal Power Spectrum Estimation. A feature extraction technique and a Neural Network classifier have been employed to determine the presence and absence of the AF episo

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32

Brierley, Matthew Joseph. "Neural network underlying snail feeding." Thesis, University of Sussex, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239132.

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33

Braun, Harald. "A neural network linking process." Thesis, City University London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410159.

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34

Lam, David Tai-Yuen. "Optically implementable neural network algorithms." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385426.

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35

Nag, Ronjon. "Neural network applications for finance." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/13819.

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Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1991.<br>Title as it appears in the M.I.T. Graduate List, Feb. 1991: Neural network applications in finance.<br>Includes bibliographical references (leaves 39-42).<br>by Ronjon Nag.<br>M.S.

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36

Hadjiprocopis, Andreas. "Feed forward neural network entities." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340374.

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37

Brown, Gavin. "Diversity in neural network ensembles." Thesis, University of Birmingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410855.

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38

Renals, Stephen John. "Speech and neural network dynamics." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/14271.

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This thesis is concerned with two principal issues. Firstly the radial basis functions (RBF) network is introduced and its properties related to other statistical and neural network classifiers. Results from a series of speech recognition experiments, using this network architecture, are reported. These experiments included a continuous speech recognition task with a 571 word lexicon. Secondly, a study of the dynamics of a simple recurrent network model is presented. This study was performed numerically, via a survey of network power spectra and a detailed investigation of the dynamics display

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39

Armstrong, James R. "Boolean weightless neural network architectures." Thesis, University of Central Lancashire, 2011. http://clok.uclan.ac.uk/5325/.

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A collection of hardware weightless Boolean elements has been developed. These form fundamental building blocks which have particular pertinence to the field of weightless neural networks. They have also been shown to have merit in their own right for the design of robust architectures. A major element of this is a collection of weightless Boolean sum and threshold techniques. These are fundamental building blocks which can be used in weightless architectures particularly within the field of weightless neural networks. Included in these is the implementation of L-max also known as N point thre

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40

Khazanova, Yekaterina. "Experiments with Neural Network Libraries." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1527607591612278.

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41

Mosher, Stephen Glenn. "Neural Network Applications in Seismology." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42329.

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Neural networks are extremely versatile tools, as evidenced by their widespread adoption into many ﬁelds in the sciences and beyond, including the geosciences. In seismology neural networks have been primarily used to automatically detect and discriminate seismic signals within time-series data, as well as provide location estimates for their sources. However, as neural network research has signiﬁcantly progressed over the past three decades, so too have its applications in seismology. Such applications now include earthquake early warning systems based on smartphone data collected from large

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42

Rimer, Michael Edwin. "Improving Neural Network Classification Training." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2094.pdf.

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43

Kabore, Raogo. "Hybrid deep neural network anomaly detection system for SCADA networks." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0190.

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Les systèmes SCADA sont de plus en plus ciblés par les cyberattaques en raison de nombreuses vulnérabilités dans le matériel, les logiciels, les protocoles et la pile de communication. Ces systèmes utilisent aujourd'hui du matériel, des logiciels, des systèmes d'exploitation et des protocoles standard. De plus, les systèmes SCADA qui étaient auparavant isolés sont désormais interconnectés aux réseaux d'entreprise et à Internet, élargissant ainsi la surface d'attaque. Dans cette thèse, nous utilisons une approche deep learning pour proposer un réseau de neurones profonds hybride efficace pour l

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44

Friji, Hamdi. "Graph neural network-based intrusion detection for secure edge networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS030.

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Face à l'escalade de la complexité et à la fréquence des cyberattaques, cette thèse propose des approches innovantes pour la détection d'intrusion dans les réseaux, en exploitant les capacités avancées des réseaux de neurones en graphe (Graph Neural Networks, GNNs) et de nouvelles représentations sous forme de graphes. Nous commençons par une analyse critique des jeux de données et des représentations de réseaux actuels, en abordant des questions clés sur leur efficacité. Nous introduisons une nouvelle représentation des flux de communication sous forme de graphes, offrant une plus grande robu

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45

Keisala, Simon. "Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143188.

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Agents being able to play board games such as Tic Tac Toe, Chess, Go and Arimaa has been, and still is, a major difficulty in Artificial Intelligence. For the mentioned board games, there is a certain amount of legal moves a player can do in a specific board state. Tic Tac Toe have in average around 4-5 legal moves, with a total amount of 255168 possible games. Both Chess, Go and Arimaa have an increased amount of possible legal moves to do, and an almost infinite amount of possible games, making it impossible to have complete knowledge of the outcome. This thesis work have created various Neu

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46

Teng, Peiyuan. "Tensor network and neural network methods in physical systems." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524836522115804.

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47

Brouwer, Roelof K. "Pattern recognition using a generalised discrete Hopfield network." Thesis, University of Warwick, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307974.

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48

Sazli, Murat Husnu Işık Can. "Neural network applications to turbo decoding." Related Electronic Resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2003. http://wwwlib.umi.com/cr/syr/main.

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49

Li, Keh-Tsong, and 李克聰. "Neural Network combined with Genetic Algorithm-Evolutionary Neural Network." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/37236508646662658444.

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碩士<br>國立交通大學<br>電機與控制工程系<br>87<br>This thesis presents a Real-Coded Rank-Based Genetic Algorithm (RCRBGA), which is represented by a chromosome containing parameters in floating-point. The use of rank-based fitness increases the population diversity. The offspring are generated by the rank-based reproduction, real parametric crossover and mutation in the evolving process. Besides, an Evolutionary Neural Network (ENN) which combines RCRBGA and Back-Propagation (BP) is introduced. ENN applies the learning concept to the evolution process, like the behavior of human beings. It not only impro

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50

"Restoration network design and neural network." Chinese University of Hong Kong, 1992. http://library.cuhk.edu.hk/record=b5887053.

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by Leung Lee.<br>Thesis (M.Sc.)--Chinese University of Hong Kong, 1992.<br>Includes bibliographical references.<br>Chapter SECTION 1. --- Introduction --- p.1<br>Chapter SECTION 2. --- Formulation of Problem --- p.2<br>Chapter 2.1 --- Problem Identification --- p.2<br>Chapter 2.2 --- Network Planning Parameters and Assumptions --- p.3<br>Chapter 2.3 --- Neural Network Model Transformation --- p.5<br>Chapter 2.4 --- Algorithm and Implementation --- p.12<br>Chapter SECTION 3. --- Simulation Results --- p.15<br>Chapter 3.1 --- All Link Costs Are Same or Nearly the Same --- p.17<br>Chapter

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