Dissertations / Theses on the topic 'Neural network programming'
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Howse, Samuel. "Dynamic programming problems, neural network solutions and economic applications." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0009/MQ60678.pdf.
Full textCollins, Tamar L. "A methodology for engineering neural network systems." Thesis, University of Exeter, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284620.
Full textSulaiman, Md Nasir. "The design of a neural network compiler." Thesis, Loughborough University, 1994. https://dspace.lboro.ac.uk/2134/25628.
Full textLukashev, A. "Basics of artificial neural networks (ANNs)." Thesis, Київський національний університет технологій та дизайну, 2018. https://er.knutd.edu.ua/handle/123456789/11353.
Full textSims, Pauline. "Turing's P-type machine and neural network hybrid systems." Thesis, University of Ulster, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240712.
Full textHaggett, Simon J. "Towards a multipurpose neural network approach to novelty detection." Thesis, University of Kent, 2008. https://kar.kent.ac.uk/24133/.
Full textHeaton, Jeff T. "Automated Feature Engineering for Deep Neural Networks with Genetic Programming." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/994.
Full textGueddar, T. "Neural network and multi-parametric programming based approximation techniques for process optimisation." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1432138/.
Full textMyers, Catherine E. "Learning with delayed reinforcement in an exploratory probabilistic logic neural network." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/46462.
Full textLategano, Antonio. "Image-based programming language recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/22208/.
Full textVisaggi, Salvatore. "Multimodal Side-Tuning for Code Snippets Programming Language Recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22993/.
Full textFerroni, Nicola. "Exact Combinatorial Optimization with Graph Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17502/.
Full textConti, Matteo. "Machine Learning Based Programming Language Identification." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20875/.
Full textWilgenbus, Erich Feodor. "The file fragment classification problem : a combined neural network and linear programming discriminant model approach / Erich Feodor Wilgenbus." Thesis, North-West University, 2013. http://hdl.handle.net/10394/10215.
Full textMSc (Computer Science), North-West University, Potchefstroom Campus, 2013
Hanselmann, Thomas. "Approximate dynamic programming with adaptive critics and the algebraic perceptron as a fast neural network related to support vector machines." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2003. http://theses.library.uwa.edu.au/adt-WU2004.0005.
Full textCheng, Chao. "Application of Artificial Neural Networks in the Power Split Controller For a Series Hydraulic Hybrid Vehicle." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1278610645.
Full textHytychová, Tereza. "Evoluční návrh neuronových sítí využívající generativní kódování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445478.
Full textMoles, Joshua Stephen. "Chemical Reaction Network Control Systems for Agent-Based Foraging Tasks." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2203.
Full textSvobodová, Jitka. "Neuronové sítě a evoluční algoritmy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218221.
Full textCampos, Jose Roberto. "Desenvolvimento de um sistema dinâmico para predição de cargas elétricas por redes neurais através do paradigma de programação orientada a objeto sob a linguagem JAVA /." Ilha Solteira : [s.n.], 2010. http://hdl.handle.net/11449/87104.
Full textBanca: Maria do Carmo Gomes da Silveira
Banca: Gelson da. Cruz Junior
Resumo: A previsão de carga, considerada essencial no planejamento da operação energética e nos estudos de ampliação e reforços da rede básica, assume importância estratégica na extensão comercial, valorizando os processos de armazenamento desses dados e da extração de conhecimentos através de técnicas computacionais. Nos últimos anos, diversos trabalhos foram publicados sobre sistemas de previsão de cargas (demanda) elétricas. Nos horizontes de curto, médio e longo prazo, os modelos neurais, estão entre os mais explorados. O objetivo deste trabalho é apresentar um sistema previsor de cargas elétricas de forma simples e eficiente através de sistemas baseados em redes neurais artificiais com treinamento realizado pelo algoritmo back-propagation. Para isto, optou-se pelo desenvolvimento de um software utilizando os paradigmas de programação orientada a objetos para criar um modelo neural de fácil manipulação, e que de certa forma, consiga corrigir o problema dos mínimos locais. Em geral, o sistema desenvolvido é capaz de atribuir os parâmetros da rede neural de forma automática através de processos exaustivos. Os resultados apresentados foram comparados utilizando outros trabalhos em que também se usaram-se os dados da mesma companhia elétrica. Este trabalho apresentou um ganho de desempenho bem satisfatório em relação a outros trabalhos encontrados na literatura para a mesma classe de problemas
Abstract: Load Forecasting is essential in planning and operation of power systems, in enlarging and reinforcing the basic network, is also very important commercially, valorizing the filing process of these data and extracting knowledge by computational techniques. Lately, several works have been published about electrical load forecasting. Short term, medium term and long term horizons are equally studied. The objective of this work is to present an electrical load forecasting system, which is simple and efficient and based on artificial neural networks whose training is with the back-propagation algorithm. Therefore, a software is developed using the paradigms of the object oriented programming technique to create a neural model which is ease to manipulate, and able to correct the local minimum problem. This system attributes the neural parameters automatically by exhaustive procedures. Results are compared with other works that have used the same data and this work presents a satisfactory performance when compared with those and others found in the literature
Mestre
Maragno, Donato. "Optimization with machine learning-based modeling: an application to humanitarian food aid." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21621/.
Full textGuazzelli, Cauê Sauter. "Modelos e métodos para estudos de configuração de redes logísticas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3138/tde-13072018-112347/.
Full textThis thesis deals with the supply chain network design problem (SCND) that aims to find the optimal location of facilities and the allocation of customers to each facility. The work considers a typical process of SCND in which discrete optimization models are run and its results are used in the decision making. The goal of the thesis is to propose models and methods to support the stages of this type of planning process. Initially, methods for the selection of candidates considered in the localization models are proposed. The methods consider the distribution of the demand points throughout the network to obtain the candidates and are evaluated by their application to two sets of scientific literature instances and comparison of computational times and objective function values. The results show that the average computational time has been reduced by 57% and the resulting objective function gaps are less than 0,16% compared to the solutions obtained by the models that consider all the demand points as candidates. In addition, the thesis present methods capable of obtaining high-quality alternative solutions to location problems that can be compared in order to provide better support for decision making. The methods obtain the K-best solutions of location problems and are evaluated by their application to 215 instances of the scientific literature. In addition, the proposed approach allowed the analysis of results never before obtained for a well-studied problem: the best solutions of the capacitated fixed cost facility location problem. Two main insights were identified: the number of facilities is stable - in 99% of the tested instances the standard deviation of the number of facilities in the 20 best solutions of each instance is less than one - and most of the selected facilities in the optimal solution of each instance is selected in most of the 20 best solutions as well. Based on these conclusions, the work investigates some general properties of localization problems and presents a topological analysis of the 215 instances, based on proposed indicators. Finally, three types of neural network models capable of identifying relations between the instances indicators and the values of the variables of the best solutions are applied and evaluated. The approach consists in comparing the computational time and the objective function value of models whose feasible solution spaces are reduced based on the results obtained by the neural networks. The results show that it is possible to use such approach to improve the SCND process, either at the construction stage of the models or by providing more information for the decision making.
Campos, Jose Roberto [UNESP]. "Desenvolvimento de um sistema dinâmico para predição de cargas elétricas por redes neurais através do paradigma de programação orientada a objeto sob a linguagem JAVA." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/87104.
Full textCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A previsão de carga, considerada essencial no planejamento da operação energética e nos estudos de ampliação e reforços da rede básica, assume importância estratégica na extensão comercial, valorizando os processos de armazenamento desses dados e da extração de conhecimentos através de técnicas computacionais. Nos últimos anos, diversos trabalhos foram publicados sobre sistemas de previsão de cargas (demanda) elétricas. Nos horizontes de curto, médio e longo prazo, os modelos neurais, estão entre os mais explorados. O objetivo deste trabalho é apresentar um sistema previsor de cargas elétricas de forma simples e eficiente através de sistemas baseados em redes neurais artificiais com treinamento realizado pelo algoritmo back-propagation. Para isto, optou-se pelo desenvolvimento de um software utilizando os paradigmas de programação orientada a objetos para criar um modelo neural de fácil manipulação, e que de certa forma, consiga corrigir o problema dos mínimos locais. Em geral, o sistema desenvolvido é capaz de atribuir os parâmetros da rede neural de forma automática através de processos exaustivos. Os resultados apresentados foram comparados utilizando outros trabalhos em que também se usaram-se os dados da mesma companhia elétrica. Este trabalho apresentou um ganho de desempenho bem satisfatório em relação a outros trabalhos encontrados na literatura para a mesma classe de problemas
Load Forecasting is essential in planning and operation of power systems, in enlarging and reinforcing the basic network, is also very important commercially, valorizing the filing process of these data and extracting knowledge by computational techniques. Lately, several works have been published about electrical load forecasting. Short term, medium term and long term horizons are equally studied. The objective of this work is to present an electrical load forecasting system, which is simple and efficient and based on artificial neural networks whose training is with the back-propagation algorithm. Therefore, a software is developed using the paradigms of the object oriented programming technique to create a neural model which is ease to manipulate, and able to correct the local minimum problem. This system attributes the neural parameters automatically by exhaustive procedures. Results are compared with other works that have used the same data and this work presents a satisfactory performance when compared with those and others found in the literature
Filho, Edson Costa de Barros Carvalho. "Investigation of Boolean neural networks on a novel goal-seeking neuron." Thesis, University of Kent, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277285.
Full textRoddier, Nicolas. "Global optimization via neural networks and D.C. programming." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186617.
Full textTurner, Andrew. "Evolving artificial neural networks using Cartesian genetic programming." Thesis, University of York, 2015. http://etheses.whiterose.ac.uk/12035/.
Full textDay, Charles Robert. "Symbol processing in RAAM neural networks." Thesis, Keele University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296075.
Full textHodge, Victoria J. "Integrating information retrieval & neural networks." Thesis, University of York, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.247019.
Full textChan, Chee Keong. "Langrange programming neural networks for nonlinear Volterra system identification." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266354.
Full textTjeng, Vincent. "Evaluating robustness of neural networks with mixed integer programming." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119563.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 43-47).
Neural networks have demonstrated considerable success on a wide variety of real-world problems. However, neural networks can be fooled by adversarial examples -- slightly perturbed inputs that are misclassified with high confidence. Verification of networks enables us to gauge their vulnerability to such adversarial examples. We formulate verification of piecewise-linear neural networks as a mixed integer program. Our verifier finds minimum adversarial distortions two to three orders of magnitude more quickly than the state-of-the-art. We achieve this via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available. The computational speedup enables us to verify properties on convolutional networks with an order of magnitude more ReLUs than had been previously verified by any complete verifier, and we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l[infinity] norm e = 0:1. On this network, we find an adversarial example for 4.38% of samples, and a certificate of robustness for the remainder. Across a variety of robust training procedures, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack for every network.
by Vincent Tjeng.
M. Eng.
Rodriguez, Adelein. "A NEAT Approach to Genetic Programming." Master's thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2379.
Full textM.S.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering MSCpE
Heaton, Jeff. "Automated Feature Engineering for Deep Neural Networks with Genetic Programming." Thesis, Nova Southeastern University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10259604.
Full textFeature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model's predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature engineering. Expressions that combine one or more of the original features usually create these engineered features. The choice of the exact structure of an engineered feature is dependent on the type of machine learning model in use. Previous research demonstrated that various model families benefit from different types of engineered feature. Random forests, gradient-boosting machines, or other tree-based models might not see the same accuracy gain that an engineered feature allowed neural networks, generalized linear models, or other dot-product based models to achieve on the same data set.
This dissertation presents a genetic programming-based algorithm that automatically engineers features that increase the accuracy of deep neural networks for some data sets. For a genetic programming algorithm to be effective, it must prioritize the search space and efficiently evaluate what it finds. This dissertation algorithm faced a potential search space composed of all possible mathematical combinations of the original feature vector. Five experiments were designed to guide the search process to efficiently evolve good engineered features. The result of this dissertation is an automated feature engineering (AFE) algorithm that is computationally efficient, even though a neural network is used to evaluate each candidate feature. This approach gave the algorithm a greater opportunity to specifically target deep neural networks in its search for engineered features that improve accuracy. Finally, a sixth experiment empirically demonstrated the degree to which this algorithm improved the accuracy of neural networks on data sets augmented by the algorithm's engineered features.
MBITI, JOHN N. "Deep learning for portfolio optimization." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104567.
Full textInnes, Andrew, and andrew innes@defence gov au. "Genetic Programming for Cephalometric Landmark Detection." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080221.123310.
Full textHowarth, Martin. "An investigation of task level programming for robotic assembly." Thesis, Nottingham Trent University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241831.
Full textKerin, Michael A. "Self-organisation and autonomous learning in logical neural networks." Thesis, Brunel University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303172.
Full textTurega, Michael A. "A parallel computer architecture to support artificial neural networks." Thesis, University of Manchester, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316469.
Full textWorthy, Paul James. "Investigation of artificial neural networks for forecasting and classification." Thesis, City University London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264247.
Full textStasinakis, Charalampos. "Applications of hybrid neural networks and genetic programming in financial forecasting." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4921/.
Full textHussain, Jabbar. "Deep Learning Black Box Problem." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393479.
Full textFeurstein, Markus, and Martin Natter. "Neural networks, stochastic dynamic programming and a heuristic for valuing flexible manufacturing systems." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/1106/1/document.pdf.
Full textSeries: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Craddock, Rachel Joy. "Multi layered radial basis function networks and the application of state space control theory to feedforward neural networks." Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360753.
Full textAlahakoon, Lakpriya Damminda 1968. "Data mining with structure adapting neural networks." Monash University, School of Computer Science and Software Engineering, 2000. http://arrow.monash.edu.au/hdl/1959.1/7987.
Full textCooper, Brenton S. "On the performance of optimisation networks /." Adelaide, 1996. http://web4.library.adelaide.edu.au/theses/09PH/09phc7758.pdf.
Full textVasconcelos, Germano Crispim. "An investigation of feedforward neural networks with respect to the detection of spurious patterns." Thesis, University of Kent, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262546.
Full textValiveti, Natana Carleton University Dissertation Computer Science. "Parallel computational geometry on Analog Hopfield Networks." Ottawa, 1992.
Find full textTownsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.
Full textCooper, Brenton S. "On the performance of optimisation networks / by Brenton S. Cooper." Thesis, Adelaide, 1996. http://hdl.handle.net/2440/18979.
Full textxi, 131 leaves : ill. ; 30 cm.
This thesis examines the performace of optimisation networks. The main objectives are to determine if there exist any factors which limit the solution quality that may be achieved with optimisation networks, to determine the reasons for any such limitations and to suggest remedies for them.
Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1996
Neri, Giacomo. "Deep neural networks for solving time prediction in mixed-integer linear programming: an experimental study." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
Find full textGarret, Aaron Dozier Gerry V. "Neural enhancement for multiobjective optimization." Auburn, Ala., 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Computer_Science_and_Software_Engineering/Dissertation/Garrett_Aaron_55.pdf.
Full text