Tesis sobre el tema "Backpropagation"
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Civelek, Ferda N. (Ferda Nur). "Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm". Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278824/.
Texto completoYee, Clifford Wing Wei Physics Faculty of Science UNSW. "Point source compensation ??? a backpropagation method for underwater acoustic imaging". Awarded by:University of New South Wales. School of Physics, 2003. http://handle.unsw.edu.au/1959.4/20590.
Texto completoBendelac, Shiri. "Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation". Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83714.
Texto completoMaster of Science
Bonnell, Jeffrey A. "Implementation of a New Sigmoid Function in Backpropagation Neural Networks". Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etd/1342.
Texto completoHövel, Christoph A. "Finanzmarktprognose mit neuronalen Netzen : Training mit Backpropagation und genetisch-evolutionären Verfahren /". Lohmar ; Köln : Eul, 2003. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=010635637&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Texto completoSeifert, Christin y Jan Parthey. "Simulation Rekursiver Auto-Assoziativer Speicher (RAAM) durch Erweiterung eines klassischen Backpropagation-Simulators". Thesis, Universitätsbibliothek Chemnitz, 2003. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200300536.
Texto completoSam, Iat Tong. "Theory of backpropagation type learning of artificial neural networks and its applications". Thesis, University of Macau, 2001. http://umaclib3.umac.mo/record=b1446702.
Texto completopotter, matthew james. "Improving ANN Generalization via Self-Organized Flocking in conjunction with Multitasked Backpropagation". NCSU, 2003. http://www.lib.ncsu.edu/theses/available/etd-03242003-075528/.
Texto completoWellington, Charles H. "Backpropagation neural network for noise cancellation applied to the NUWES test ranges". Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26899.
Texto completoThis thesis investigates the application of backpropagation neural networks as an alternative to adaptive filtering at the NUWES test ranges. To facilitate the investigation, a model of the test range is developed. This model accounts for acoustic transmission losses, the effects of doppler shift, multipath, and finite propagation time delay. After describing the model, the backpropagation neural network algorithm and feature selection for the network are explained. Then, two schemes based on the network's output, signal waveform recovery and binary code recovery, are applied to the model. Simulation results of the signal waveform recovery and direct code recovery schemes are presented for several scenarios.
Seifert, Christin Parthey Jan. "Simulation Rekursiver Auto-Assoziativer Speicher (RAAM) durch Erweiterung eines klassischen Backpropagation-Simulators". [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10607558.
Texto completoSchilling, Glenn D. "Modeling Aircraft Fuel Consumption with a Neural Network". Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36533.
Texto completoMaster of Science
Jones, Lloyd H. "Machinery monitoring and diagnostics using pseudo Wigner-Ville distribution and backpropagation neural network". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA276219.
Texto completoFernandes, Luiz Gustavo Leao. "Utilização de redes neurais na análise e previsão de séries temporais". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1995. http://hdl.handle.net/10183/25774.
Texto completoThis work presents a study of the prediction power of Artificial Neural Networks (ANN) in comparison with prediction capability of traditional Time Series models, more specifically the Unobservable Components Models (UCM). The data used to perform the study was the monthly american airlines passengers, the annual rainfall in Fortaleza, Brazil and the monthly gross industrial output for the state of Rio Grande do Sul, Brazil. The results show that Artificial Neural Networks can outperform the forecasts of Unobservable Components Models.
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.
Texto completoEvolutionära algoritmer uppnår bra prestanda för ett stort antal olika typer av optimeringsproblem. I detta examensprojekt har ett nätverksoptimeringsproblem lösts genom omformulering och vidareutveckling av angreppssättet. Ett förslag till ramverk har utformats och implementerats för att träna neuronnätverk i övervakade inlärningsscenarier. För evolutionära algoritmer används en väldefinierad träningsfunktion för att utvärdera nätverksparametrar, och en noggrant härledd form av approximerade gradienter används för att uppdatera parametrarna. Ramverkets prestanda har testats genom att träna två olika typer av linjära affina respektive konvolutionära neuronnätverk, för optimering av telefonnummerverifiering. I detta applikationsscenario förutses om en telefonnummerverifiering kommer att lyckas eller inte med hjälp av ett neuronnätverk som i sig är ett binärt klassificeringsproblem. Dessutom har dess prestanda också jämförts med traditionella backpropagationsoptimerare från två aspekter: noggrannhet och hastighet. Resultaten visar att detta ramverk kan driva en nätverksträningsprocess för att konvergera till en viss nivå. Trots brus och fluktuationer konvergerar både noggrannhet och förlust till ungefär under samma mönster som i backpropagation. Dessutom verkar den evolutionära algoritmen ha högre uppdateringseffektivitet per tidsenhet i det första träningsskedet innan den konvergerar. När det gäller finjustering fungerar det inte lika bra som backpropagation under den sista konvergensperioden.
Fischer, Manfred M. y Sucharita Gopal. "Learning in Single Hidden Layer Feedforward Network Models: Backpropagation in a Real World Application". WU Vienna University of Economics and Business, 1994. http://epub.wu.ac.at/4192/1/WSG_DP_3994.pdf.
Texto completoSeries: Discussion Papers of the Institute for Economic Geography and GIScience
Markham, Ina Samanta. "An exploration of the robustness of traditional regression analysis versus analysis using backpropagation networks". Diss., This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-06062008-170305/.
Texto completoMurray, Andrew Gerard William y n/a. "Micro-net the parallel path artificial neuron". Swinburne University of Technology, 2006. http://adt.lib.swin.edu.au./public/adt-VSWT20070423.121528.
Texto completoHettinger, Christopher James. "Hyperparameters for Dense Neural Networks". BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7531.
Texto completoHerrmann, Kai, Hannes Voigt, Thorsten Seyschab y Wolfgang Lehner. "InVerDa - co-existing Schema Versions Made Foolproof". IEEE, 2016. https://tud.qucosa.de/id/qucosa%3A75285.
Texto completoStaufer-Steinnocher, Petra y Manfred M. Fischer. "A Neural Network Classifier for Spectral Pattern Recognition. On-Line versus Off-Line Backpropagation Training". WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/4152/1/WSG_DP_6097.pdf.
Texto completoSeries: Discussion Papers of the Institute for Economic Geography and GIScience
Oliver, Muncharaz Javier. "MODELIZACIÓN DE LA VOLATILIDAD CONDICIONAL EN ÍNDICES BURSÁTILES : COMPARATIVA MODELO EGARCH VERSUS RED NEURONAL BACKPROPAGATION". Doctoral thesis, Editorial Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/35803.
Texto completoOliver Muncharaz, J. (2014). MODELIZACIÓN DE LA VOLATILIDAD CONDICIONAL EN ÍNDICES BURSÁTILES : COMPARATIVA MODELO EGARCH VERSUS RED NEURONAL BACKPROPAGATION [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/35803
Alfresco
Batbayar, Batsukh y S3099885@student rmit edu au. "Improving Time Efficiency of Feedforward Neural Network Learning". RMIT University. Electrical and Computer Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090303.114706.
Texto completoGaspar, Thiago Lombardi. "Reconhecimento de faces humanas usando redes neurais MLP". Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-27042006-231620/.
Texto completoThis research presents a facial recognition algorithm based in neural networks. The algorithm contains two main modules: one for feature extraction and another for face recognition. It was applied in digital images from three database, PICS, ESSEX and AT&T, where the face was previously detected. The method for feature extraction was based on previously knowledge of the facial components location (eyes and nose) and on the application of the horizontal and vertical signature for the identification of these components. The mean result obtained for this module was 86.6% for the three database. For the recognition module it was used the multilayer perceptron architecture (MLP), and for training this network it was used the backpropagation algorithm. The extracted facial features were applied to the input of the neural network, that identified the face as belonging or not to the database with 97% of hit ratio. Despite the good results obtained it was verified that the MLP could not distinguish facial features with very close values. Therefore the MLP is not the most efficient network for this task
U, San Cho. "Trading simulations on stock market by backpropagation learning of artificial neural networks and traditional linear regression". Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1447318.
Texto completoAl-Serhan, Hasan Muaidi. "Extraction of Arabic word roots : an approach based on computational model and multi-backpropagation neural networks". Thesis, De Montfort University, 2008. http://hdl.handle.net/2086/4921.
Texto completoHudson, Erik Mark. "A Portable Computer System for Recording Heart Sounds and Data Modeling Using a Backpropagation Neural Network". UNF Digital Commons, 1995. http://digitalcommons.unf.edu/etd/158.
Texto completoRola, Marcelo Coleto. "Previsão da geração de energia elétrica no médio prazo para o Estado do Rio Grande do Sul empregando redes neurais artificiais". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/157828.
Texto completoThe demand and, consequently, the generation of electric power are very important issues for social and economic development of countries. Models to forecast these parameters in long and medium terms are used to anticipate possible sceneries and propose strategies for the energy planning of countries. In this context, the present study aims to forecast the generation of electric energy in Rio Grande do Sul State (RS) in a medium-term horizon (one year) using, Artificial Neural Networks (ANNs) of the feedforward type with algorithm of supervised learning backpropagation. For the development of this work, a script was elaborated in order to execute the necessary simulations, which were carried out through Matlab® software. The selected variables of influence as inputs of forecasting model refer to economy (State and National), to the electric energy balance and to the meteorology State, during the period from January, 2009 to March, 2016. In order to train the neural network, this data set was added to the entrance matrix, with monthly frequency, from January, 2009 to March, 2015 and for prediction, data were inserted from April, 2015 to March, 2016. Finally, after RNA complete simulation, the observed result of the electric power generation of the State was compared with the one obtained through the prediction model, indicating a mean absolute percent error (MAPE) of 5.86% and a mean absolute deviation (MAD) of 134.15 average MW. The obtained results in this work are promising, besides; they are similar to those found in literature, in this way demonstrating the reliability and efficacy of the using method.
Chen, Jianhua. "NEURAL NETWORK APPLICATIONS IN AGRICULTURAL ECONOMICS". UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_diss/228.
Texto completoFischer, Manfred M. y Petra Staufer-Steinnocher. "Optimization in an Error Backpropagation Neural Network Environment with a Performance Test on a Pattern Classification Problem". WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/4150/1/WSG_DP_6298.pdf.
Texto completoSeries: Discussion Papers of the Institute for Economic Geography and GIScience
Draghici, Sorin. "Using constraints to improve generalisation and training of feedforward neural networks : constraint based decomposition and complex backpropagation". Thesis, University of St Andrews, 1996. http://hdl.handle.net/10023/13467.
Texto completoScarborough, David J. (David James). "An Evaluation of Backpropagation Neural Network Modeling as an Alternative Methodology for Criterion Validation of Employee Selection Testing". Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277752/.
Texto completoTrnkóci, Andrej. "Programová knihovna pro práci s umělými neuronovými sítěmi s akcelerací na GPU". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236155.
Texto completoViecheneski, Rodrigo. "APLICAÇÃO DE REDES NEURAIS ARTIFICIAIS NO TRATAMENTO DE DADOS AGROMETEOROLÓGICOS VISANDO A CORREÇÃO DE SÉRIES TEMPORAIS". UNIVERSIDADE ESTADUAL DE PONTA GROSSA, 2012. http://tede2.uepg.br/jspui/handle/prefix/157.
Texto completoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
This dissertation presents the development of a computational system called System for Treatment of Agrometeorological weather Series (STST Agrometeorológicas), with the objective of treating agrometeorological data in order to correct time weather. For the development of the study some data were collected from the agrometeorological stations, provided by Fundação ABC. The stations were located in the state of Paraná, in the cities of Ponta Grossa (long - 49.95025733, lat - 25.30156819) and Castro (long -49.8672, lat -24.6752). The computational system that has been suggested made use of the technology of Artificial Neural Networks on the type of Multilayer Perceptron and the backpropagation training algorithm of backpropagation error. It was developed with the Object Pascal programming language, using the integrated development environment Embarcadero Delphi 2009. To validate the proposed method we conducted six case studies, and the one which presented the best result for agrometeorological variable average temperature was the first case study of Castro's weather station, with a hit percentage between the treated registers and the registers without failure of 96.5%, a Pearson correlation coefficient of 0.98 and a simple average of the errors obtained from the training the neural network of 0.026406. The average errors of the neural networks was calculated between the values of errors obtained in each training during a period of correction failure. For the agrometeorological variable relative humidity, the best result was found in the case study 5 of Castro’s weather station, with a hit percentage of 95.7%, a Pearson correlation coefficient of 0.97 and the simple average of the errors obtained from the training the neural network of 0,094298. Given this context, it was revealed that the STST Agrometeorological is a viable alternative in the treatment of meteorological variables such as temperature and relative humidity, since there were results with hit percentage greater than 95% in the treatments of fails of the weather series studied.
Esta dissertação apresenta o desenvolvimento de um sistema computacional deno-minado Sistema para Tratamento de Séries Temporais Agrometeorológicas (STST Agrometeorológicas), com o objetivo de tratar dados agrometeorológicos visando a correção de séries temporais. Para o desenvolvimento dos estudos foram utilizados dados de estações agrometeorológicas disponibilizados pela Fundação ABC, situa-da no estado do Paraná, nas cidades de Ponta Grossa (long -49.95025733, lat -25.30156819) e Castro (long -49.8672, lat -24.6752). O sistema computacional pro-posto fez uso da tecnologia de Redes Neurais Artificiais do tipo Perceptron de Múlti-plas Camadas e do algoritmo backpropagation de treinamento de retropropagação do erro. E foi desenvolvido com a linguagem de programação Object Pascal, utili-zando o ambiente de desenvolvimento integrado Embarcadero Delphi 2009. Para validar o método proposto, foram realizados seis estudos de caso, dentre os quais, o que apresentou o melhor resultado para variável agrometeorológica temperatura média foi o estudo de caso 1 da estação agrometeorológica de Castro, com um per-centual de acerto entre os registros tratados e os registros sem falha de 96,5%, um coeficiente de correlação de Pearson de 0,98 e uma média simples entre os erros obtidos nos treinamentos da rede neural de 0,026406. A média dos erros das redes neurais foi calculada entre os valores dos erros obtidos em cada treinamento, duran-te a correção de um determinado período de falha. Para variável agrometeorológica umidade relativa do ar, o melhor resultado encontrado foi o estudo de caso 5 da es-tação agrometeorológica de Castro, com um percentual de acerto de 95,7%, um coe-ficiente de correlação de Pearson de 0,97 e a média simples dos erros da rede neu-ral de 0,094298. Diante desse contexto, foi possível perceber que o STST Agrome-teorológicas é uma alternativa viável no tratamento das variáveis agrometeorológicas temperatura média e umidade relativa do ar, uma vez que, houve resultados com percentual de acerto superior a 95% no tratamento de falhas das séries temporais estudadas.
Anderson, Thomas. "Built-In Self Training of Hardware-Based Neural Networks". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1512039036199393.
Texto completoHansson, Jonas. "Image analysis, an approach to measure grass roots from images". Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-592.
Texto completoIn this project a method to analyse images is presented. The images document the development of grassroots in a tilled field in order to study the movement of nitrate in the field. The final aim of the image analysis is to estimate the volume of dead and living roots in the soil. Since the roots and the soil have a broad and overlapping range of colours the fundamental problem is to find the roots in the images. Earlier methods for analysis of root images have used methods based on thresholds to extract the roots. To use a threshold the pixels of the object must have a unique range of colours separating them from the colour of the background, this is not the case for the images in this project. Instead the method uses a neural network to classify the individual pixels. In this paper a complete method to analyse images is presented and although the results are far from perfect, the method gives interesting results
Hsieh, Yuan-Chang y 謝元章. "A Pipeline Backpropagation Neuro-Microprocessor". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/91143767222762737465.
Texto completo大葉大學
電機工程學系碩士班
94
The study develops a pipelined 32-bit microprocessor embedded with first-order back-propagation neural network and MIPS-like architecture by using Algorithmic State Machine(ASM) and Verilog HDL. The designed neural network is verified by using Matlab. The Matlab source code is derived into MIPS-like assembly and machine code to be embedded in processor core. With the comparison of the simulation result of SynaptiCAD and Matlab, the verified processor core is further synthesized by using Xilinx FPGA development software. The VLSI layout of developed neuro-microprocessor is implemented under TSMC 0.18 um process technology at final.
Mnih, Andriy. "Learning nonlinear constraints with contrastive backpropagation". 2004. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=94951&T=F.
Texto completoYang, Gu Ming y 顧明陽. "Lp norm backpropagation for adaptive equalizer". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/64225443979136227447.
Texto completo"Variable background born inversion by wavefield backpropagation". Laboratory for Information and Decision Systems, Massachusetts Institute of Technology], 1986. http://hdl.handle.net/1721.1/2918.
Texto completoLin, Bo-Wei y 林柏威. "Reconfigurable Backpropagation Neural Network Implementation for FPGA". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/44687836984947728873.
Texto completo國立暨南國際大學
電機工程學系
96
In this thesis, we proposed reconfigurable back propagation neural network (BPNN) hardware architecture. This architecture makes BPNN has more flexibility. It can process many complex applications and avoid synthesis again. User writes instructions into the Program Memory (PM) to reconfigure neural network architecture. The single neuron computation architecture executes reconfigurable BPNN hardware architecture. This computation architecture achieves resource sharing and reduces area in hardware. We proposed new reconfigurable feed-forward neural network hardware architecture. The purpose of new architecture reduces number of hidden layers in multilayer feed-forward neural network. The computation architecture is the same as BPNN hardware architecture. Finally, it uses Xilinx – ISE to synthesis BPNN and verification. This architecture verification and comparison are in the field-programmable gate arrays (FPGAs).
Lo, Guo-Jhang y 羅國彰. "Windows Programming of Backpropagation Artificial Neural Network". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/59899233059137113146.
Texto completo國立臺北科技大學
化學工程系碩士班
92
In recent years, the artificial neural networks have been applied on many different fields, such like nonlinear regression analysis, weather forecast, etc. However, most of that are commercial software, it is not cheap for beginner. The object of this study is to develop a windows program of feedforward artificial neural network for beginner to use. Visual Basic is used to develop this package. Backpropagation algorithm of Bernard Widrow and Marcian Hoff is used to train parameters of neural networks. The Nguyen-Widrow method is used to set the initial values of the network parameters. The limitations of the program are: the number of hidden layer is only one, the hidden layer of transform function is Hyperbolic Tangent or Sigmoid function, the output layer transform function is Linear. Even though this software has so many limitations, it have proved that the applications of this package on many testing examples have good performances.
Lin, Bo-Wei. "Reconfigurable Backpropagation Neural Network Implementation for FPGA". 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0020-0907200812121500.
Texto completoHuang, Jeng-Horng y 黃正宏. "Implementation of Distributed Backpropagation in a CORBA Environment". Thesis, 2000. http://ndltd.ncl.edu.tw/handle/28167589994066286843.
Texto completo國立中正大學
資訊工程研究所
88
Learning plays an important role in neural computing, but it takes long time when the input data set is large and complex. Many papers have proposed how to implement learning algorithms on parallel machines or a cluster of computers to reduce learning time in the past. In this thesis, we present a distributed backpropagation learning that distributes the data set to learn in a cluster of computers. Our experiment results reveal that the error calculated by it is closer with the convention pattern mode backpropagation learning, and the time used by it is faster when the data is complex. Due to that the development and maintenance of distributed applications using conventional techniques are time-consuming, and that the applications may not be extensible, we use the CORBA technique as our implementation middleware. It provides a framework that seamlessly integrates heterogeneous objects. Thus, we can efficiently implement our distributed backpropagation learning on a cluster of computers.
Chih-Yuan, Chen. "A Multi-level Backpropagation Network for Pattern Recognition Systems". 1993. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611360486.
Texto completoPowell, Alan Roy. "Application of backpropagation-like generative algorithms to various problems". Thesis, 1992. http://hdl.handle.net/10413/5619.
Texto completoThesis (M.Sc.)-University of Natal, Durban, 1992.
Lin, Yi-Ting y 林逸婷. "Backpropagation Neural Network Model for Stock Trading Points Prediction". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/61766390205991572629.
Texto completo國立高雄第一科技大學
金融理財研究所
99
According to the high development of technology and internet, many of stock data are digitalized. Hence, it becomes convenience and fast to obtain the data from file transfer; however, the huge and complicated information are hard to be systemized and analyzed by human beings in a short time. Artificial intelligence (AI) techniques are excellent in dealing with the complicated problems; therefore, they could be the tools of predicting and analyzing the stock market information. The Backpropagation Neural Network (BPN) approach rapidly rises in these years, especially using in finance area such as, the prediction of stock prices, financial crisis prediction, the forecasting of exchange rate movement, and portfolio management, the performance is outstanding. In this research, several technical indicators are applied to analysis of a large number of historical data in order to enhance the predictability of the particular stocks. According to the technical indices, they are inputted to the BPN to train the model. Therefore, the possible turning points could be detected by BPN. Besides, the technical indicators including Stochastic, Relative Strength Index (RSI), Moving Average Convergence and Divergence (MACD), Directional Movement Index (DMI), Deviation rate (BIAS), foreign capital, and the suspension of margin purchase. The results of this research show that the combination of different indicators using the BPN approach is superior to the buy and hold strategy but still cannot reach positive returns of the target stocks after a period of training. As the result, the study provides a statement that even though the BPN approach is good at forecasting stock price in finance area; the input factors still play a significant role in determining the accuracy of trading decisions. In brief, settle on the appropriate input factor is still a crucial lesson for researching in the future.
Mong-Tao-Tsai y 蔡孟陶. "The study of convergency analysis for backpropagation neural network". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/79255621222935149969.
Texto completoJin, Guo-Bin y 金國斌. "Applying Backpropagation Neural Networks to GPS Navigation Satellite Selection". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/72449385533612576981.
Texto completo國立海洋大學
航運技術研究所
89
For the GPS navigation and positioning, in order to improve satellite geometry and then improve accuracy, it is desirable to use all of the signals of the satellites in view except those with too low elevation angles. Because of the geometric relationships between the receiver position and the satellite positions, certain satellites are actually not effective in raising the whole positioning accuracy. It is, nevertheless, very time-consuming in the positioning process. Four satellites or more will generally be required for GPS position fix. Some receiver hardware may be limited to processing limited number of visible satellites. Therefore, it is sometimes necessary to select the optimal satellite subset. Geometry Dilution of Precision (GDOP) is an indicator of the quality of the geometry of the satellite constellation. It will be viewed as the multiplicative factor that magnifies ranging error. A smaller GDOP indicates that the geometry is better, which yield a better positioning accuracy. Matrix inversion will be required for computing GDOP. The GDOP will reach a minimum value when using all satellites in view, however it is very time-consuming especially when the number of satellites is large. Besides, the addition of satellites will not all raise accuracy effectively. In this paper, the application of backpropagation neural networks (BPNN) to GPS satellite GDOP approximation is presented. The BPNN can handle the non-linear mapping to avoid matrix inversion and choose the satellite subset that minimizes the GDOP. The proposed algorithms for GDOP approximation will provide an efficient alternative method for optimal satellite subset selection.
Wu, I. Kang y 吳毅剛. "Designing Fuzzy Neural Network PID Controllers by Backpropagation Algorithms". Thesis, 1996. http://ndltd.ncl.edu.tw/handle/33347754690292742478.
Texto completoYoung, Chi-Jou y 楊啟洲. "Risk Prediction of Credit Loan Using Backpropagation Nwural Network". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/28503255685667719504.
Texto completo中華大學
科技管理研究所
93
According to the Bureau of Monetary Affairs, Financial Supervisory Commission in Taiwan, as of the end of December 2001, the average of nonperforming loan ratio of all banks had reached a historical high. As the financial leverage effect of the banks is getting worse, and the rate of interest is decreasing, the interest collected can no more cover the loss caused by the bad debts. Credit guaranty has becomes one of the important means, under the severe competition nowadays, to avoid as well as predict the risk of loan. Traditionally approaches using statistic or mathematic model to accomplish the risk-avoiding task, such as discriminant analysis and logistic regression have limited themselves to a stricter environment or background, which is lack of adaptability in reality. In this paper, a neural network trained by the backpropagation paradigm (BPN) is utilized as a tool for predicting the risk in credit guaranty. We enumerate 37 discriminated variables, partly theoretical and empirical, as the input variables for the neural network. The data were collected from a financial institute, where those between 1999 and 2002 were used for training and between 2003 and 2005 were used for testing. As a result, The BPN achieved a correct prediction rate of nearly 100% in predicting the attribute of the loaner. The proposed model is suitable for a decision-support tool in granting loans; furthermore, it establishes the groundwork for value-created activities in the customer relation management.