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1

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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2

Koessler, Denise Renee. "A Predictive Model for Secondary RNA Structure Using Graph Theory and a Neural Network." Digital Commons @ East Tennessee State University, 2010. https://dc.etsu.edu/etd/1684.

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In this work we use a graph-theoretic representation of secondary RNA structure found in the database RAG: RNA-As-Graphs. We model the bonding of two RNA secondary structures to form a larger structure with a graph operation called merge. The resulting data from each tree merge operation is summarized and represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not based on the merge data vector. The network correctly assigned a high probability of RNA-likeness to trees identified as RNA-like in the RAG database

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3

Calvert, David. "A distance-based neural network model for sequence processing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0010/NQ30591.pdf.

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Ozkok, Yusuf Ibrahim. "Web Based Ionospheric Forecasting Using Neural Network And Neurofuzzy Models." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606031/index.pdf.

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This study presents the implementation of Middle East Technical University Neural Network (METU-NN) models for the ionospheric forecasting together with worldwide usage capability of the Internet. Furthermore, an attempt is made to include expert information in the Neural Network (NN) model in the form of neurofuzzy network (NFN). Middle East Technical University Neurofuzzy Network (METU-NFN) modeling approach is developed which is the first attempt of using a neurofuzzy model in the ionospheric forecasting studies. The Web based applications developed in this study have the ability to be cust

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5

FUTIA, GIUSEPPE. "Neural Networks forBuilding Semantic Models and Knowledge Graphs." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850594.

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Thiruvengadachari, Sathish. "Experimental and neural network-based model for human-machine systems reliability." Diss., Online access via UMI:, 2006.

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7

Zorzetto, Luiz Flavio Martins. "Bioprocess monitoring with hybrid neural network/mechanistic model based state estimators." Thesis, University of Nottingham, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283350.

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8

Wang, Feng. "Neural network model of memory reinforcement for text-based intelligent tutoring system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0021/NQ30122.pdf.

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9

Wredh, Simon. "Neural Network Based Model Predictive Control of Turbulent Gas-Solid Corner Flow." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420056.

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Over the past decades, attention has been brought to the importance of indoor air quality and the serious threat of bio-aerosol contamination towards human health. A novel idea to transport hazardous particles away from sensitive areas is to automatically control bio-aerosol concentrations, by utilising airflows from ventilation systems. Regarding this, computational fluid dynamics (CFD) may be employed to investigate the dynamical behaviour of airborne particles, and data-driven methods may be used to estimate and control the complex flow simulations. This thesis presents a methodology for ma

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10

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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11

Samal, Mahendra Engineering &amp Information Technology Australian Defence Force Academy UNSW. "Neural network based identification and control of an unmanned helicopter." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43917.

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This research work provides the development of an Adaptive Flight Control System (AFCS) for autonomous hover of a Rotary-wing Unmanned Aerial Vehicle (RUAV). Due to the complex, nonlinear and time-varying dynamics of the RUAV, indirect adaptive control using the Model Predictive Control (MPC) is utilised. The performance of the MPC mainly depends on the model of the RUAV used for predicting the future behaviour. Due to the complexities associated with the RUAV dynamics, a neural network based black box identification technique is used for modelling the behaviour of the RUAV. Auto-regres

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12

Boetticher, Gary. "A neural network-based bottom-up approach for building a software reuse economic model." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=994.

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Thesis (Ph. D.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains viii, 226 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 152-159).

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13

Öberg, Oskar. "Critical Branching Regulation of the E-I Net Spiking Neural Network Model." Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76770.

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Spiking neural networks (SNN) are dynamic models of biological neurons, that communicates with event-based signals called spikes. SNN that reproduce observed properties of biological senses like vision are developed to better understand how such systems function, and to learn how more efficient sensor systems can be engineered. A branching parameter describes the average probability for spikes to propagate between two different neuron populations. The adaptation of branching parameters towards critical values is known to be important for maximizing the sensitivity and dynamic range of SNN. In

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14

Bahremand, Saeid. "Blood Glucose Management Streptozotocin-Induced Diabetic Rats by Artificial Neural Network Based Model Predictive Control." Thesis, Southern Illinois University at Edwardsville, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10249804.

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<p> Diabetes is a group of metabolic diseases where the body’s pancreas does not produce enough insulin or does not properly respond to insulin produced, resulting in high blood sugar levels over a prolonged period. There are several different types of diabetes, but the most common forms are type 1 and type 2 diabetes. Type 1 diabetes Mellitus (T1DM) can occur at any age, but is most commonly diagnosed from infancy to late 30s. If a person is diagnosed with type 1 diabetes, their pancreas produces little to no insulin, and the body’s immune system destroys the insulin-producing cel

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15

Pracný, Vladislav. "Neural network based shock absorber model with a thermodynamical coupling : experiment, modeling and vehicle simulation /." Aachen : Shaker, 2009. http://d-nb.info/994209967/04.

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16

Figueroa, Barraza Joaquín Eduardo. "A capsule neural network based model for structural damage localization and quantification using transmissibilty data." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170185.

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Tesis para optar al grado de Magíster en Ciencias de la Ingeniería Mención Mecánica<br>Memoría para optar al título de Ingeniero Civil Mecánico<br>Dentro de la ingeniería estructural, el monitoreo de condición usando diferentes tipos de sensores ha sido importante en la prevención de fallas y diagnóstico del estado de salud. El desafío actual es aprovechar al máximo las grandes cantidades de datos para entregar mediciones y predicciones precisas. Los algoritmos de aprendizaje profundo abordan estos problemas mediante el uso de datos para encontrar relaciones complejas entre ellos. Entre estos

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17

Rockney, Alissa Ann. "A Predictive Model Which Uses Descriptors of RNA Secondary Structures Derived from Graph Theory." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etd/1300.

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The secondary structures of ribonucleic acid (RNA) have been successfully modeled with graph-theoretic structures. Often, simple graphs are used to represent secondary RNA structures; however, in this research, a multigraph representation of RNA is used, in which vertices represent stems and edges represent the internal motifs. Any type of RNA secondary structure may be represented by a graph in this manner. We define novel graphical invariants to quantify the multigraphs and obtain characteristic descriptors of the secondary structures. These descriptors are used to train an artificial neural

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18

Keisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.

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Using AI to automatically describe images is a challenging task. The aim of this study has been to compare the use of character-based language models with one of the current state-of-the-art token-based language models, im2txt, to generate image captions, with focus on morphological correctness. Previous work has shown that character-based language models are able to outperform token-based language models in morphologically rich languages. Other studies show that simple multi-layered LSTM-blocks are able to learn to replicate the syntax of its training data. To study the usability of character

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19

McKinnell, L. A. "A neural network based ionospheric model for the bottomside electron density profile over Grahamstown, South Africa." Thesis, Rhodes University, 2003. http://hdl.handle.net/10962/d1005262.

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This thesis describes the development and application of a neural network based ionospheric model for the bottomside electron density profile over Grahamstown, South Africa. All available ionospheric data from the archives of the Grahamstown (33.32ºS, 26.50ºE) ionospheric station were used for training neural networks (NNs) to predict the parameters required to produce the final profile. Inputs to the model, called the LAM model, are day number, hour, and measures of solar and magnetic activity. The output is a mathematical description of the bottomside electron density profile for that partic

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20

Shamsudin, Syariful Syafiq. "The Development of Neural Network Based System Identification and Adaptive Flight Control for an AutonomousHelicopter System." Thesis, University of Canterbury. Mechanical Engineering Department, 2013. http://hdl.handle.net/10092/8803.

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This thesis presents the development of self adaptive flight controller for an unmanned helicopter system under hovering manoeuvre. The neural network (NN) based model predictive control (MPC) approach is utilised in this work. We use this controller due to its ability to handle system constraints and the time varying nature of the helicopter dynamics. The non-linear NN based MPC controller is known to produce slow solution convergence due to high computation demand in the optimisation process. To solve this problem, the automatic flight controller system is designed using the NN based approxi

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21

Paradza, Masimba Wellington. "Development of a neural network based model for predicting the occurrence of spread F within the Brazilian sector." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005245.

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Spread F is a phenomenon of the ionosphere in which the pulses returned from the ionosphere are of a much greater duration than the transmitted ones. The occurrence of spread F can be predicted using the technique of Neural Networks (NNs). This thesis presents the development and evaluation of NN based models (two single station models and a regional model) for predicting the occurrence of spread F over selected stations within the Brazilian sector. The input space for the NNs included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity)

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22

Pracný, Vladislav [Verfasser]. "Neural network-based shock absorber model with a thermodynamical coupling : Experiment, modeling and vehicle simulation / Vladislav Pracny." Aachen : Shaker, 2009. http://d-nb.info/1161302549/34.

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Kadlec, Jakub. "Creating a prediction model for weather forecasting based on artificial neural network supported by association rules mining." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-203981.

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This diploma thesis introduces three different methods of creating a neural network binary classifier for the purpose of automated weather prediction with attribute pre-selection using association rules and correlation patters mining by the LISp-Miner system. First part of the thesis consists of collection of theoretical knowledge enabling the creation of such predictive model, whereas the second part describes the creation of the model itself using the CRISP-DM methodology. Final part of the thesis analyses the performance of created classifiers and concludes the proposed methods and their po

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24

Garagnani, Max. "Understanding language and attention : brain-based model and neurophysiological experiments." Thesis, University of Cambridge, 2009. https://www.repository.cam.ac.uk/handle/1810/243852.

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This work concerns the investigation of the neuronal mechanisms at the basis of language acquisition and processing, and the complex interactions of language and attention processes in the human brain. In particular, this research was motivated by two sets of existing neurophysiological data which cannot be reconciled on the basis of current psycholinguistic accounts: on the one hand, the N400, a robust index of lexico-semantic processing which emerges at around 400ms after stimulus onset in attention demanding tasks and is larger for senseless materials (meaningless pseudowords) than for matc

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25

Kim, Jun Ha. "Artificial neural network (ANN) based decision support model for alternative workplace arrangements (AWA) readiness assessment and type selection." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31830.

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Thesis (Ph.D)--Building Construction, Georgia Institute of Technology, 2010.<br>Committee Chair: Roper, Kathy; Committee Co-Chair: Kangari, Roozbeh; Committee Member: Ashuri, Baabak; Committee Member: Castro, Daniel; Committee Member: Rouse, William. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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26

Thanikasalam, Kokul. "Appearance based online visual object tracking." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130875/1/Kokul_Thanikasalam_Thesis.pdf.

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This thesis presents research contributions to the field of computer vision based visual object tracking. This study investigates appearance based object tracking by using traditional hand-crafted and deep features. The thesis proposes a real-time tracking framework with high accuracy which follows a deep similarity tracking strategy. This thesis also proposes several deep tracking frameworks for high-accuracy tracking and to manage the spatial information loss. The research findings of the study would be able to be used in a range of applications including visual surveillance systems.

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27

Wilgenbus, 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.

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The increased use of digital media to store legal, as well as illegal data, has created the need for specialized tools that can monitor, control and even recover this data. An important task in computer forensics and security is to identify the true le type to which a computer le or computer le fragment belongs. File type identi cation is traditionally done by means of metadata, such as le extensions and le header and footer signatures. As a result, traditional metadata-based le object type identi cation techniques work well in cases where the required metadata is available and unaltered

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Biruk, David D. "Neural Network Based Control of Integrated Recycle Heat Exchanger Superheaters in Circulating Fluidized Bed Boilers." UNF Digital Commons, 2013. http://digitalcommons.unf.edu/etd/470.

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The focus of this thesis is the development and implementation of a neural network model predictive controller to be used for controlling the integrated recycle heat exchanger (Intrex) in a 300MW circulating fluidized bed (CFB) boiler. Discussion of the development of the controller will include data collection and preprocessing, controller design and controller tuning. The controller will be programmed directly into the plant distributed control system (DCS) and does not require the continuous use of any third party software. The intrexes serve as the loop seal in the CFB as well as intermedi

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29

Chin-Lang, Huang, and 黃錦郎. "Neural Network Based Model Reference Adaptive Control." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/25112423970598183022.

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碩士<br>義守大學<br>電機工程學系<br>90<br>Adaptive controllers have been used in industrial field for many years. The Model Reference Adaptive Control is an important control method in adaptive control field. The key problem with MRAS (Model Reference Adaptive System) is to determine the adjustment mechanism. The Mechanism for adjusting the parameters in a MRAC can be obtained in two ways: one is using a gradient method (also called MIT rule), the other is applying Lyapunov stability theory. The MIT rule has one parameter, the adaptation gain, that must be chosed by the user. It is difficult to find a Lya

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30

Huang, Ming-Hung, and 黃明宏. "Grid based Artificial Neural Network Typhoon Rainfall Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/71095795825027771927.

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碩士<br>國立成功大學<br>水利及海洋工程學系碩博士班<br>97<br>Typhoon is one of the most important source of water resources of Taiwan. Typhoon brought abundant rainfall to its covering area. It is generally regarded moving path, characteristics of typhoon, local terrain and meteorological factors of a gage station as major factors affect rainfall depth. There are many approaches were developed to simulated the complicated non-linear process of typhoon rainfall. However, the accuracy of simulation may be improved. 　　A back-propagation artificial neural network model (BPN) was adopted in this thesis to simulate t

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31

高正一. "A neural network model based on fuzzy classification concept." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/52510456915064071054.

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32

Yu, Hong-Kai, and 游弘凱. "Prediction of PID Phenomenon based on Neural Network Model." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/76823974246043135494.

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碩士<br>國立宜蘭大學<br>電機資訊學院碩士在職專班<br>104<br>Potential Induced Degradation (PID) is a kind of induced attenuation phenomenon which makes solar module panels to withstand the impact from the terminal voltage when solar cell is packaged into the PV modules and work at outdoors in sunlight to generate electricity. It will cause serious solar module panels’ power attenuation, and result in lower power generation efficiency and more serious problems when the terminal voltage is applied on solar modules, due to this phenomenon. In this study, we prepare the three kind cell of different Reflection Index, c

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33

Tsai, Jui-Ju, and 蔡睿儒. "Process Optimization Based on Neural Network Model and Orthogonal Arrays." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/89636932994073794808.

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碩士<br>中原大學<br>機械工程研究所<br>96<br>This thesis presents a systematic and cost-effective approach for process optimization with minimal experimental runs. Based on the experimental design scheme of orthogonal arrays, artificial neural network is used to establish the process model. Moreover, Taguchi-genetic algorithm (TGA) is used to search for the global optimum of the fabrication conditions. The procedure starts planning and conducting the initial experiment with fewer levels. By adding experimental points selected from augmented orthogonal arrays, the process model is corrected. This step is con

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34

Hsiao, Chih-kai, and 蕭志凱. "A neural network based model for controlling smart building skins." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/53741826763028181414.

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碩士<br>國立臺灣科技大學<br>建築系<br>96<br>Building skin defines the relation of people and natural environment. It should be adjustable to the changes of people needs and natural environment. Building skins need the ability of learning for the adaptation to different situations under variations of spatial functions, opening, surroundings and occupants of the building. This research advances a feasible framework which can realize smart building skins by providing the ability of learning and automatic controlling to satisfy the demands of people for a comfortable environment by adjusting parameters of the

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Lee, Hsin-Ni, and 李心妮. "An Integrated Neural Network Based Inspection Model of Financial Distress." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/88983574117257721857.

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Peng, Chi-Wei, and 彭志煒. "A Neural Network Based Model for Hysteresis Loop of Transformers." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/63127790094797053606.

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Wang, Hao-Cheng, and 王浩丞. "An Attention-based Neural Network Model for Interest Shift Prediction." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/j879jw.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>105<br>Recommendation systems have mainly dealt with the problem of recommending items to fit user preferences, while the dynamicity of user interest is not fully considered. We observe that music streaming platforms like YouTube always recommend songs that either from the same artist or with the same title, assuming that users have a static interest in similar items, but ignore the fact that we get satiated easily with repeated consumptions. To provide a more appealing user experience, recent developments in recommendation system have focused on introducing novelty

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Wu, Chao-Chung, and 吳肇中. "An Attention Based Neural Network Model for Unsupervised Lyrics Rewriting." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w4bcnv.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>106<br>Creative writing has become a standard task to showcase the power of artificial intelligence. This work tackles a challenging task in this area, the lyrics rewriting. This task possesses several unique challenges. First, we require the outputs to be not only semantically correlated with the original lyrics, but also coherent in segmentation structure, rhyme as the rewritten lyrics must be performed by the artist with the same music. Second, there is no parallel rewriting lyrics corpus available for supervised training. We propose a deep neural network based m

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LAI, WEI-CHENG, and 賴偉程. "Building Children Emotion Recognition Model Based on Convolutional Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/88wp5c.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>107<br>In daily life, people often express emotions through ways other than language. Among them, people’s facial expression is the most common way to express their emotion, people can take corresponding actions to achieve their goals via facial expression. Thus the emotion recognition has become an important technology in the artificial intelligence industry, the related work including Safe Driving, Commercial Advertising Evaluation, etc. Although there are several emotion recognition models for adult appeared in recent years, they didn’t have better performance on

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盧嘉弘. "An internal model control-based neural network ship steering autipilot design." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/05964257685286522082.

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碩士<br>國立海洋大學<br>航運技術研究所<br>90<br>It is well known that the ship steering dynamics is characterized by a highly complicated nonlinear behavior. For simplicity, a linear model is often adopted in the design of the steering autopilot to facilitate the design and implementation. However, to make the autopilot of practical use, the modeling error between the model and the plant under control has to be monitored and the controller parameters should be adjusted accordingly. In this work, the internal model control (IMC) configuration is adopted and the neural network (NN） is employed in de

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Ke, Ching-Shun, and 柯清順. "The Predicting and Identifying of system model based on Neural Network." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/98348418393663863026.

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碩士<br>國立臺灣海洋大學<br>電機工程學系<br>93<br>For the analysis and application of control-theory, It is necessary to model the system to a mathematic platform, according to the physical, input and output characteristic of the system. Unfortunately, the complexity of the system and uncertainty of environment cause that the model cannot exactly and accurately performs all the behavior of the target system. Therefore, the implementation cannot achieve our expected performance due to inaccurate modeling. This paper will apply the advantages of simple architecture and operation of backpropagation to setup th

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Huang, Teng Yu, and 黃登宇. "The Research of Gold Price Forecasting Model based on Neural Network." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/23183123048808260533.

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碩士<br>臺北城市科技大學<br>電子商務研究所<br>100<br>It's volatile times to international finance situation . Since the U.S. investment bank Lehman Brothers went bankrupt in 2008, the following are debt crisis in Europe and the risk of US national debt. Certainly, the events cause USD devaluation. We found that LIBOR rate & TED spread have been raised while the stock price were go down in the market. In this study, we propose two hypotheses. First, MA, RSI, two indicators help to improve the forecasting performance of gold. Second, TED spread will help enhance the performance of the gold price forecast. The ex

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Hsu, Chia-Ming, and 許家銘. "Emotional and Conditional Model for Pet Robot based on Neural Network." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/62567712120856301898.

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碩士<br>中原大學<br>資訊工程研究所<br>102<br>Recently more and more pet robot products are launched, which showing the increasing market demand for pet robots, and a plenty of researches have pointed out that the product innovativeness can encourage consumers’ acceptance, the pet robot will be made with more variability, more versatility, and more interesting in each generation. To make the robots do common activities in the same environment with human and integrate it to human’ life is the target of those researchers who were studying in the field of intelligent robot. As a matter of fact, all traditional

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Tsung-ChiehWen and 溫淙傑. "Implementation of Text Classification Model Based on Recurrent Convolutional Neural Network." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/qq5p25.

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碩士<br>國立成功大學<br>工程科學系碩士在職專班<br>105<br>In a variety of community media types of network platform have on-line operation, and the popularity of smart phones, these changes have changed the way people use the web. Over the past years, users only can search and get the information from the website, but now the user can be an information provider. Many Internet users began to be willing and keen to share their views out, so there is a lot of text data on the Internet. Ten years ago people have heard this sentence:「This is an era of information explosion」, and now because everyone can be the provide

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Huang, Chun-Chieh, and 黃駿杰. "A TFT-LCD Defect Classification Model Based on Convolutional Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/25awgz.

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碩士<br>國立交通大學<br>工業工程與管理系所<br>106<br>Since the ever-changing technology nowadays, people are increasing their request for skill and qualities as well as the display screens. As the level of the quality of display screens rise, the numbers of dead dots that used to be the inspection standard become a problem that can’t be ignored. Traditional inspection methods that inspected by humans may cause occupational injuries and fatigue and also harm the yield rate. Therefore, the trend of using Automatic Optical Inspection (AOI) instead of the traditional way is inevitable. This research is based on AO

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Hsiao, Chung-Ting, and 蕭仲廷. "A Policy-Based Reinforcement Learning Model for Neural Network Architecture Search." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/vruep9.

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碩士<br>國立中央大學<br>軟體工程研究所<br>107<br>The last few years, in the field of machine learning deep learning and its core – neural network has playing a very important role. Because of its high performance, and easy to implement, in addition to computing resource advancement such as GPU and TPU, these reason triggered a vigorous growth of deep learning and neural network. The most common problem user faced is when using deep learning which neural network structure should user use, and how can neural network structure be designed. Furthermore because of handcraft neural network need a great deal of kno

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Tsai, Tzu-Han, and 蔡子涵. "A DQN-Based Reinforcement Learning Model for Neural Network Architecture Search." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qf2jk4.

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碩士<br>國立中央大學<br>資訊管理學系<br>107<br>Designing neural network (NN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce HCM, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing NN architectures for a given learning task. The learning agent is trained to sequentially choose NN layers using DQN with an ɛ-greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and it

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Chao, Lee-Chang, and 趙豊昌. "The Yield Prediction Model with Neural Network for Integrated Circuit --- Based on Poisson Model." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/46233153779168204597.

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碩士<br>國立交通大學<br>工業工程與管理學系<br>85<br>In integrated circuit (IC) manufacturing, a wafer''s defects tend to cluster. As the wafer size increases, the clustering phenomenon of the defects becomes increasingly apparent.When the conventional Poisson yield model is used, the clustered defects frequently cause false results. In this study, we propose a neural network-based modified Poisson yield model to predict the wafer yield in IC manufacturing. The proposed approach can reduce the phenomenon of

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(8784458), Qin He. "Learning Lighting Models with Shader-Based Neural Networks." Thesis, 2020.

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<p>To correctly reproduce the appearance of different objects in computer graphics applications, numerous lighting models have been proposed over the past several decades. These models are among the most important components in the modern graphics pipeline since they decide the final pixel color shown in the generated images. More physically valid parameters and functions have been introduced into recent models. These parameters expanded the range of materials that can be represented and made virtual scenes more realistic, but they also made the lighting models more complex and dependent on me

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Stewart, IAN. "A Modified Genetic Algorithm and Switch-Based Neural Network Model Applied to Misuse-Based Intrusion Detection." Thesis, 2009. http://hdl.handle.net/1974/1720.

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As our reliance on the Internet continues to grow, the need for secure, reliable networks also increases. Using a modified genetic algorithm and a switch-based neural network model, this thesis outlines the creation of a powerful intrusion detection system (IDS) capable of detecting network attacks. The new genetic algorithm is tested against traditional and other modified genetic algorithms using common benchmark functions, and is found to produce better results in less time, and with less human interaction. The IDS is tested using the standard benchmark data collection for intrusion det

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