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Thematische Bibliographien / Evolutionary development of neural network / Dissertationen

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Dissertationen zum Thema „Evolutionary development of neural network“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Bush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.

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2

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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3

Hytychová, 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.

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The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datase

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4

Kadiyala, Akhil. "Development and Evaluation of an Integrated Approach to Study In-Bus Exposure Using Data Mining and Artificial Intelligence Methods." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341257080.

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5

Adams, Bryan (Bryan Paul) 1977. "Evolutionary, developmental neural networks for robust robotic control." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37900.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 136-143).<br>The use of artificial evolution to synthesize controllers for physical robots is still in its infancy. Most applications are on very simple robots in artificial environments, and even these examples struggle to span the "reality gap," a name given to the difference between the performance of a simulated robot and the performance of a.real robot using the same evolved controller. This dissertation describes three methods for

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6

Tsui, Kwok Ching. "Neural network design using evolutionary computing." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299918.

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7

Hayward, Serge. "Financial forecasting and modelling with an evolutionary artificial neural network." Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439394.

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8

Hlynka, Markian D. "A framework for an automated neural network designer using evolutionary algorithms." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0014/MQ41716.pdf.

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9

Jagadeesan, Ananda Prasanna. "Real time evolutionary algorithms in robotic neural control systems." Thesis, Robert Gordon University, 2006. http://hdl.handle.net/10059/436.

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This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is “off-line,” as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated L

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10

Jakobsson, Henrik. "Inversion of an Artificial Neural Network Mapping by Evolutionary Algorithms with Sharing." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-165.

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<p>Inversion of the artificial neural network mapping is a relatively unexplored field of science. By inversion we mean that a search is conducted to find what input patterns that corresponds to a specific output pattern according to the analysed network. In this report, an evolutionary algorithm is proposed to conduct the search for input patterns. The hypothesis is that the inversion with the evolutionary search-method will result in multiple, separate and equivalent input patterns and not get stuck in local optima which possibly would cause the inversion to result in erroneous answer. Besid

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11

Singh, Jaswinder. "RNA Structure Prediction using Deep Neural Network Architectures and Improved Evolutionary Profiles." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/414924.

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RNAs are important biological macro-molecules that play critical roles in many biological processes. The functionality of RNA depends on its three-dimensional (3D) structure, which further depends on its primary structure, i.e. the order of sequence of nucleotides in the RNA chain. Direct prediction of the 3D structure of an RNA from its sequence is a challenging task. Therefore, the 3D structure is further divided into two-dimensional (2D) properties such as secondary structure, contact maps and one-dimensional (1D) properties such as torsion angles and solvent accessibility. An accurate pred

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12

Westermann, Gert. "Constructivist neural network models of cognitive development." Thesis, University of Edinburgh, 2000. http://hdl.handle.net/1842/22733.

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In this thesis I investigate the modelling of cognitive development with Constructivist neural networks. I argue that the constructivist nature of development, that is, the building of a cognitive system through active interactions with its environment, is an essential property of human development and should be considered in models of cognitive development. I evaluate this claim on the basis of evidence from cortical development, cognitive development, and learning theory. In an empirical evaluation of this claim, I then present a constructivist neural network model of the acquisition of the

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13

Smith, Duncan. "An evolutionary approach to optimising neural network predictors for passive sonar target tracking." Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/26870.

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Object tracking is important in autonomous robotics, military applications, financial time-series forecasting, and mobile systems. In order to correctly track through clutter, algorithms which predict the next value in a time series are essential. The competence of standard machine learning techniques to create bearing prediction estimates was examined. The results show that the classification based algorithms produce more accurate estimates than the state-of-the-art statistical models. Artificial Neural Networks (ANNs) and K-Nearest Neighbour were used, demonstrating that this technique is no

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14

Fischer, Manfred M., and Yee Leung. "A Genetic Algorithm Based Evolutionary Computational Neural Network for Modelling Spatial Interaction Data." WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/4151/1/WSG_DP_6198.pdf.

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Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds

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15

Chan, Heather Y. "Gene Network Inference and Expression Prediction Using Recurrent Neural Networks and Evolutionary Algorithms." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2648.

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We demonstrate the success of recurrent neural networks in gene network inference and expression prediction using a hybrid of particle swarm optimization and differential evolution to overcome the classic obstacle of local minima in training recurrent neural networks. We also provide an improved validation framework for the evaluation of genetic network modeling systems that will result in better generalization and long-term prediction capability. Success in the modeling of gene regulation and prediction of gene expression will lead to more rapid discovery and development of therapeutic medici

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16

Yang, Yini. "Training Neural Networks with Evolutionary Algorithms for Flash Call Verification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283039.

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Evolutionary algorithms have achieved great performance among a wide range of optimization problems. In this degree project, the network optimization problem has been reformulated and solved in an evolved way. A feasible evolutionary framework has been designed and implemented to train neural networks in supervised learning scenarios. Under the structure of evolutionary algorithms, a well-defined fitness function is applied to evaluate network parameters, and a carefully derived form of approximate gradients is used for updating parameters. Performance of the framework has been tested by train

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17

Reiling, Anthony J. "Convolutional Neural Network Optimization Using Genetic Algorithms." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.

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18

Propst, Michael David. "Applying Linear Regression and Neural Network Meta-Models for Evolutionary Algorithm Based Simulation Optimization." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-08112009-164218/.

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The increase in computing power over the last decade has led to an increase in the use of simulation programs to model real world optimization problems as well as the complexity with which these problems can be modeled. Once a model has been built, an experimental design is often used to determine the effects certain parameters have on the problem trying to determine the good settings that optimize a set of outputs. However, these problems often have a large number of variables or parameters that can be changed with wide value ranges and as these simulation models become increasingly more comp

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19

Sirois, Sylvain. "A neural network perspective on learning and development /." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36836.

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This manuscript-based thesis explores the relationship between learning and development. The first manuscript reviews the important empirical regularities identified in human discrimination shift learning, including a qualitative age-related change in performance observed in childhood. Leading theoretical accounts of the empirical data are discussed, suggesting that none provides a comprehensive interpretation. The manuscript presents the novel, spontaneous overtraining interpretation. It hypothesizes that age-related changes in human shift learning stem from differences in amount of processin

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20

Gessaroli, Erica <1983&gt. "Development, degeneration and neural network of the bodily self." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6342/1/Gessaroli_Erica_tesi.pdf.

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The question addressed by this dissertation is how the human brain builds a coherent representation of the body, and how this representation is used to recognize its own body. Recent approaches by neuroimaging and TMS revealed hints for a distinct brain representation of human body, as compared with other stimulus categories. Neuropsychological studies demonstrated that body-parts and self body-parts recognition are separate processes sub-served by two different, even if possibly overlapping, networks within the brain. Bodily self-recognition is one aspect of our ability to distinguish betwee

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Gessaroli, Erica <1983&gt. "Development, degeneration and neural network of the bodily self." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6342/.

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The question addressed by this dissertation is how the human brain builds a coherent representation of the body, and how this representation is used to recognize its own body. Recent approaches by neuroimaging and TMS revealed hints for a distinct brain representation of human body, as compared with other stimulus categories. Neuropsychological studies demonstrated that body-parts and self body-parts recognition are separate processes sub-served by two different, even if possibly overlapping, networks within the brain. Bodily self-recognition is one aspect of our ability to distinguish betwee

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22

Mandal, Rakesh Kumar. "Development of Neural Network techniques to recognize handwritten characters." Thesis, University of North Bengal, 2015. http://ir.nbu.ac.in/handle/123456789/1790.

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23

Fieldsend, Jonathan E. "Novel algorithms for multi-objective search and their application in multi-objective evolutionary neural network training." Thesis, University of Exeter, 2003. http://hdl.handle.net/10871/11706.

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24

Van, Lierde Boris. "Developing Box-Pushing Behaviours Using Evolutionary Robotics." Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6250.

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The context of this report and the IRIDIA laboratory are described in the preface. Evolutionary Robotics and the box-pushing task are presented in the introduction.The building of a test system supporting Evolutionary Robotics experiments is then detailed. This system is made of a robot simulator and a Genetic Algorithm. It is used to explore the possibility of evolving box-pushing behaviours. The bootstrapping problem is explained, and a novel approach for dealing with it is proposed, with results presented.Finally, ideas for extending this approach are presented in the conclusion.

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25

Sung, Woong Je. "A neural network construction method for surrogate modeling of physics-based analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43721.

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A connectivity adjusting learning algorithm, Optimal Brain Growth (OBG) was proposed. Contrast to the conventional training methods for the Artificial Neural Network (ANN) which focus on the weight-only optimization, the OBG method trains both weights and connectivity of a network in a single training process. The standard Back-Propagation (BP) algorithm was extended to exploit the error gradient information of the latent connection whose current weight has zero value. Based on this, the OBG algorithm makes a rational decision between a further adjustment of an existing connection weight and a

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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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Lapedriza, Alberto. "Gene regulatory network of melanocyte development." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.690724.

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Greenhill et al. (2011) developed a gene regulatory network of the main genes and interactions known to play a role in melanocyte biology, and generated a mathematical model to describe the behaviour of this complex network using semi quantitative data (ISH expression data). In this project we sought to collect expression data from four genes of the melanocyte GRN (sox10, kit, mitfa and dct) to develop a quantitative model that is able to describe the data more accurately. Moreover, we intended to identify more genes that are part of the melanocyte development process to be incorporated to the

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28

Southworth, David. "A simple artificial neural network development system for study and research." Master's thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-02162010-020120/.

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29

Bseiso, Abdallah. "DEVELOPMENT OF ARTIFICIAL NEURAL NETWORK SOFTWARE AND MODELS FOR ENGINEERING MATERIALS." Cleveland State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=csu1610197805407219.

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30

Kramer, Gregory Robert. "An analysis of neutral drift's effect on the evolution of a CTRNN locomotion controller with noisy fitness evaluation." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1182196651.

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31

Marshall, Stephen James. "The development of neural network based decision aids for clinical decision support." Thesis, University of Sheffield, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245570.

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32

Gualano, Leonardo. "Development of artificial neural network techniques for prediction of wheel-rail forces." Thesis, Manchester Metropolitan University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436651.

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Over the past two decades, Artificial Neural Network (ANN) techniques have been used in many fields of research, due to their better robustness and fail ure tolerance capabilities compared with conventional modelling approaches. In applications such as railway vehicle dynamics, discrete scenarios of vehicle/track interactions are currently modelled using computer packages such as Simpack, ADAMS Rail, Vampire or Medyna, which use multi-body techniques to accurately model different aspects of rail vehicles and tracks such as derailment or passenger comfort. This thesis presents novel ANN techniq

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Son, Jungbaeg. "Development of Reliable Long-term Bridge Deterioration Model using Neural Network Techniques." Thesis, Griffith University, 2010. http://hdl.handle.net/10072/367833.

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Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a Decision Support System (DSS), have been developed since the early 1990’s to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for p

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34

Massera, Gianluca. "Evolution of grasping behaviour in anthropomorphic robotic arms with embodied neural controllers." Thesis, University of Plymouth, 2012. http://hdl.handle.net/10026.1/1172.

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The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced se

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Tepvorachai, Gorn. "An Evolutionary Platform for Retargetable Image and Signal Processing Applications." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1209504058.

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36

Kuchar, Olga Anna. "Development of animated finger movements via a neural network for tendon tension control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq39322.pdf.

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37

Ding, ZhengHao. "Development and Application of Swarm Intelligence and Neural Network Techniques for Structural Identification." Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/81005.

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This thesis develops novel swarm intelligence approaches to conduct structural identification, including structural damage identification and nonlinear parameter identification. Comprehensive numerical and experimental verifications demonstrate the necessity and rationality of the developed modifications and the effectiveness and performance of using the proposed approaches for various structural identification problems.

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Abou, Rayan Ihab Samy. "Development and evaluation of neural network models for cost reduction in unmanned air vehicles." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/10880.

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With a growing demand for cost reduction in unmanned air vehicles (UAVs), there has been considerable interest in exploiting existing aircraft technologies. This thesis focuses on two technologies: model-based sensor fault detection, isolation and accommodation (SFDIA) schemes and flush air data sensing (FADS) systems. In the aerospace industry, SFDIA is traditionally based on physical (sensor) redundancy. Unfortunately this approach can be inadequate in UAVs due to cost, weight and space implications. Consequently researchers have found the concept of ‘virtual’ sensor redundancy (i.e. model-b

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Attal, Asadullah. "Development of Neural Network Models for Prediction of Highway Construction Cost and Project Duration." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1282146503.

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40

Schliebs, Stefan. "Heterogeneous probabilistic models for optimisation and modelling of evolving spiking neural networks." AUT University, 2010. http://hdl.handle.net/10292/963.

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This thesis proposes a novel feature selection and classification method employing evolving spiking neural networks (eSNN) and evolutionary algorithms (EA). The method is named the Quantum-inspired Spiking Neural Network (QiSNN) framework. QiSNN represents an integrated wrapper approach. An evolutionary process evolves appropriate feature subsets for a given classification task and simultaneously optimises the neural and learning-related parameters of the network. Unlike other methods, the connection weights of this network are determined by a fast one-pass learning algorithm which dramaticall

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Wu, Dong-Perng, and 吳東朋. "Evolutionary Neural Network Development and Optimization." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/90531512107411022471.

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碩士<br>國立交通大學<br>資訊科學學系<br>84<br>Neural Networks and Genetic Algorithms are two kind of famous Computational Intelligence Models. They have shown their ability in many research domains. In this thesis, we combine these two technologies to solve pattern recognition problems. In biology, bacteria can change their expression of genes in order to save energy. Based on this observation, we combine a new approach called conditional genes with genetic algorithmsto achieve neural network structur

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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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阮呂正璽. "MeNet : A Multi-Objective Evolutionary Artificial Neural Network." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/47873963540026950168.

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碩士<br>長庚大學<br>資訊管理研究所<br>90<br>Interest in algorithms which dynamically construct artificial neural networks has been growing in recent years. The traditional methods that have been used to construct near optimal artificial neural networks are to minimize the sequence of error functions associated with the growing network. But, constructing a near optimal artificial neural networks by considering the sequence of error unilaterally, will lead to more complicated network architecture. This paper proposes an evolutionary system for constructing neural networks named “MeNet”. Combining

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Tsai, Kuen-Yan, and 蔡昆洋. "An Evolutionary Approach toward Self-Organization of Neural Network." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/67772871322786701226.

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碩士<br>國立交通大學<br>資訊科學學系<br>86<br>The construction of a proper network architecture is always a tough challenge while we apply neural networks. In this thesis, we propose a construction method inspired by biological nervous systems, which is developed based on the construction rules and information encoded in genes. According to this idea, we design the construction rules for neural networks and encode the rules. Then, evolve the optimal rules by using genetic algorithms.The spirit of rule co

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李宜勳. "Evolutionary Learning and Application of BMF Fuzzy-Neural Network." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/13551677512428832309.

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碩士<br>輔仁大學<br>電子工程學系<br>90<br>In this thesis, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a simplified genetic algorithm (SGA) is proposed. Fuzzy-neural networks (FNN) are traditionally trained by using gradient-based methods, and may fall into a local minimum during the learning process. Genetic algorithms have drawn significant attentions in various fields due to their capabilities of directed random search for global optimization. This motivates the use of genetic algorithms to overcome the prob

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Wang, Chao-Chi, and 王炤棋. "Evolutionary Computation and Parallel Neural Network for Channel Assignment." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/69306607788362415125.

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楊世宏. "Neural Network with Evolutionary Structure Learning and Its Prediction Application." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/34255734360192730143.

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博士<br>國立交通大學<br>電控工程研究所<br>100<br>This dissertation proposes a feedforward-neural-network-aided grey model (FNAGM) and its related on-line parameter learning and structure learning algorithms. The FNAGM uses a first-order single variable grey model (GM(1,1)) to predict signal and adopts a feedforward neural network (NN) to compensate the prediction error of GM(1,1). Furthermore, an on-line batch training is proposed to update the weights of NN in real-time. Thus, FNAGM can precisely predict and adapt itself to the dynamical change of the signal. To design the structure of FNAGM efficiently, a

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Sudjono, Erick, and 謝德祥. "Evolutionary Fuzzy Hybrid Neural Network for Decision-Making in Construction Management." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/75977105749001014871.

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碩士<br>國立臺灣科技大學<br>營建工程系<br>96<br>Many studies have found that High-order neural network (HONN) is enables to boost neural network performance. This research utilize a hybrid model with HONN and Linear Neural Network (NN) concepts to develop high-order and linear neural connectors for layer connections. Consequently, this developed HNN will involve a linear/nonlinear switch for each neural layer connection. Furthermore, fuzzy logic (FL) has already been introduced to neural network and it is also found that the combination of FL and FNN has been proof-reading. Furthermore, fuzzy logic (FL) also

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Wei-Chong, Cheng, and 鄭維崇. "The Study of Evolutionary Artificial Neural Network for Mobile Robot Navigation." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/51595137777333455129.

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碩士<br>大同工學院<br>機械工程研究所<br>86<br>Different kind of intelligent learning rules have been applied todevelop intelligent robots recently. An intelligent robot should beable to learn to behave itself in an unknown environment using theinput information obtained from sensors. Clearly, the more complex theenvironment, the more difficult the learning task. Evolutionaryartificial neural network (EANN), a combination of artificial neuralnetwork and evolutionary search procedures, is applied in this study todevelop intelligent mobile robot due to it''s adaptability to changingenvironment. EANN is a

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(9818684), NULL McLeod. "Neural network based ensemble classifier for digital mammography." Thesis, 2016. https://figshare.com/articles/thesis/Neural_network_based_ensemble_classifier_for_digital_mammography/13437824.

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Breast cancer is a debilitating condition that has a high mortality and morbidity rate and is the most commonly diagnosed form of cancer in women. The aetiology of the disease is not known however mitigating lifestyle and genetic conditions are known to increase the likelihood of contracting the condition. This thesis proposes a number of techniques designed to increase the diagnostic accuracy of Computer Aided Diagnostic (CADx) systems. The first technique involved breaking down the benign and malignant classes into sub-classes through clustering and using a support vector machine classifier

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