Log in

Relevant bibliographies by topics / Artificial neural network / Dissertations / Theses

Dissertations / Theses on the topic 'Artificial neural network'

To see the other types of publications on this topic, follow the link: Artificial neural network.

Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 dissertations / theses for your research on the topic 'Artificial neural network.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.

1

BRUCE, WILLIAM, and OTTER EDVIN VON. "Artificial Neural Network Autonomous Vehicle : Artificial Neural Network controlled vehicle." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191192.

Full text

Abstract:

This thesis aims to explain how a Artificial Neural Network algorithm could be used as means of control for a Autonomous Vehicle. It describes the theory behind the neural network and Autonomous Vehicles, and how a prototype with a camera as its only input can be designed to test and evaluate the algorithms capabilites, and also drive using it. The thesis will show that the Artificial Neural Network can, with a image resolution of 100 × 100 and a training set with 900 images, makes decisions with a 0.78 confidence level.
Denna rapport har som mal att beskriva hur en Artificiellt Neuronnatverk al- goritm kan anvandas for att kontrollera en bil. Det beskriver teorin bakom neu- ronnatverk och autonoma farkoster samt hur en prototyp, som endast anvander en kamera som indata, kan designas for att testa och utvardera algoritmens formagor. Rapporten kommer visa att ett neuronnatverk kan, med bildupplos- ningen 100 × 100 och traningsdata innehallande 900 bilder, ta beslut med en 0.78 sakerhet.

APA, Harvard, Vancouver, ISO, and other styles

2

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Chambers, Mark Andrew. "Queuing network construction using artificial neural networks /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193665234291.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Alkharobi, Talal M. "Secret sharing using artificial neural network." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/1223.

Full text

Abstract:

Secret sharing is a fundamental notion for secure cryptographic design. In a secret sharing scheme, a set of participants shares a secret among them such that only pre-specified subsets of these shares can get together to recover the secret. This dissertation introduces a neural network approach to solve the problem of secret sharing for any given access structure. Other approaches have been used to solve this problem. However, the yet known approaches result in exponential increase in the amount of data that every participant need to keep. This amount is measured by the secret sharing scheme information rate. This work is intended to solve the problem with better information rate.

APA, Harvard, Vancouver, ISO, and other styles

6

Zhao, Lichen. "Random pulse artificial neural network architecture." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0006/MQ36758.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Parzhin, Yu, А. Rohovyi, and V. Nevliudova. "Detector Artificial Neural Network. Neurobiological rationale." Thesis, ХНУРЕ, 2019. http://openarchive.nure.ua/handle/document/10037.

Full text

Abstract:

On the basis of the formulated hypotheses the information model of a neuron-detector is suggested, the detector being one of the basic elements of a detector artificial neural network (DANN). The paper subjects the connectionist paradigm of ANN building to criticism and suggests a new presentation paradigm for ANN building and neuroelements (NE) learning. The adequacy of the suggested model is proved by the fact that is does not contradict the modern propositions of neuropsychology and neurophysiology.

APA, Harvard, Vancouver, ISO, and other styles

8

Ng, Justin. "Artificial Neural Network-Based Robotic Control." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1846.

Full text

Abstract:

Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.

APA, Harvard, Vancouver, ISO, and other styles

9

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Lukashev, A. "Basics of artificial neural networks (ANNs)." Thesis, Київський національний університет технологій та дизайну, 2018. https://er.knutd.edu.ua/handle/123456789/11353.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Brunger, Clifford A. "Artificial neural network modeling of damaged aircraft." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283227.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Tang, Chuan Zhang. "Artificial neural network models for digital implementation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq30298.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Tupas, Ronald-Ray Tiñana. "Artificial neural network modelling of filtration performance." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0011/MQ59890.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Luan, Wenpeng. "Voltage ranking using artificial neural network method." Thesis, University of Strathclyde, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366960.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Bataineh, Mohammad Hindi. "Artificial neural network for studying human performance." Thesis, University of Iowa, 2012. https://ir.uiowa.edu/etd/3259.

Full text

Abstract:

The vast majority of products and processes in industry and academia require human interaction. Thus, digital human models (DHMs) are becoming critical for improved designs, injury prevention, and a better understanding of human behavior. Although many capabilities in the DHM field continue to mature, there are still many opportunities for improvement, especially with respect to posture- and motion-prediction. Thus, this thesis investigates the use of artificial neural network (ANN) for improving predictive capabilities and for better understanding how and why human behave the way they do. With respect to motion prediction, one of the most challenging opportunities for improvement concerns computation speed. Especially, when considering dynamic motion prediction, the underlying optimization problems can be large and computationally complex. Even though the current optimization-based tools for predicting human posture are relatively fast and accurate and thus do not require as much improvement, posture prediction in general is a more tractable problem than motion prediction and can provide a test bead that can shed light on potential issues with motion prediction. Thus, we investigate the use of ANN with posture prediction in order to discover potential issues. In addition, directly using ANN with posture prediction provides a preliminary step towards using ANN to predict the most appropriate combination of performance measures (PMs) - what drives human behavior. The PMs, which are the cost functions that are minimized in the posture prediction problem, are typically selected manually depending on the task. This is perhaps the most significant impediment when using posture prediction. How does the user know which PMs should be used? Neural networks provide tools for solving this problem. This thesis hypothesizes that the ANN can be trained to predict human motion quickly and accurately, to predict human posture (while considering external forces), and to determine the most appropriate combination of PM(s) for posture prediction. Such capabilities will in turn provide a new tool for studying human behavior. Based on initial experimentation, the general regression neural network (GRNN) was found to be the most effective type of ANN for DHM applications. A semi-automated methodology was developed to ease network construction, training and testing processes, and network parameters. This in turn facilitates use with DHM applications. With regards to motion prediction, use of ANN was successful. The results showed that the calculation time was reduced from 1 to 40 minutes, to a fraction of a second without reducing accuracy. With regards to posture prediction, ANN was again found to be effective. However, potential issues with certain motion-prediction tasks were discovered and shed light on necessary future development with ANNs. Finally, a decision engine was developed using GRNN for automatically selecting four human PMs, and was shown to be very effective. In order to train this new approach, a novel optimization formulation was used to extract PM weights from pre-existing motion-capture data. Eventually, this work will lead to automatically and realistically driving predictive DHMs in a general virtual environment.

APA, Harvard, Vancouver, ISO, and other styles

16

Choi, Hyunjong. "Medical Image Registration Using Artificial Neural Network." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1523.

Full text

Abstract:

Image registration is the transformation of different sets of images into one coordinate system in order to align and overlay multiple images. Image registration is used in many fields such as medical imaging, remote sensing, and computer vision. It is very important in medical research, where multiple images are acquired from different sensors at various points in time. This allows doctors to monitor the effects of treatments on patients in a certain region of interest over time. In this thesis, artificial neural networks with curvelet keypoints are used to estimate the parameters of registration. Simulations show that the curvelet keypoints provide more accurate results than using the Discrete Cosine Transform (DCT) coefficients and Scale Invariant Feature Transform (SIFT) keypoints on rotation and scale parameter estimation.

APA, Harvard, Vancouver, ISO, and other styles

17

Baker, Thomas Edward. "Implementation limits for artificial neural networks." Full text open access at:, 1990. http://content.ohsu.edu/u?/etd,268.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

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.

Full text

Abstract:

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)
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
Nenhuma
Normalmente Redes Neurais Artificiais (RNAs) necessitam estações de trabalho para o seu processamento, por causa da complexidade do sistema. Este tipo de arquitetura de processamento requer que instrumentos de campo estejam localizados na vizinhança da estação de trabalho, caso exista a necessidade de processamento em tempo real, ou que o dispositivo de campo possua como única tarefa a de coleta de dados para processamento futuro. Este projeto visa criar uma arquitetura em lógica programável para um neurônio genérico, no qual as RNAs podem fazer uso da natureza paralela de FPGAs para executar a aplicação de forma rápida. Este trabalho mostra que a utilização de lógica programável para a implementação de RNAs de baixa resolução de bits é viável e as redes neurais, devido à natureza paralelizável, se beneficiam pela implementação em hardware, podendo obter resultados de forma muito rápida.
Currently, modern Artificial Neural Networks (ANN), according to their complexity, require a workstation for processing all their input data. This type of processing architecture requires that the field device is located somewhere in the vicintity of a workstation, in case real-time processing is required, or that the field device at hand will have the sole task of collecting data for future processing, when field data is required. This project creates a generic neuron architecture in programmabl logic, where Artifical Neural Networks can use the parallel nature of FPGAs to execute applications in a fast manner, albeit not using the same resolution for its otputs. This work shows that the utilization of programmable logic for the implementation of low bit resolution ANNs is not only viable, but the neural network, due to its parallel nature, benefits greatly from the hardware implementation, giving fast and accurate results.

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Xu, Shuxiang. "Neuron-adaptive neural network models and applications /." [Campbelltown, N.S.W. : The Author], 1999. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030702.085320/index.html.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Beckenkamp, Fábio Ghignatti. "A component architecture for artificial neural network systems." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=964923580.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Leong, Cheok Fan. "Approximation theory of multilayer feedforward artificial neural network." Thesis, University of Macau, 2002. http://umaclib3.umac.mo/record=b1446728.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Theramongkol, Phunsak. "Intelligent ozone-level forecasting using artificial neural network." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0021/MQ54752.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Zahra, Fathima. "Artificial neural network approach to transmission line relaying." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0001/MQ42465.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Thirkell, Lawrence Alexander. "An artificial neural network approach to authorship determination." Thesis, Heriot-Watt University, 1993. http://hdl.handle.net/10399/1418.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Johnson, D. E. "Analogue VLSI implementation of an artificial neural network." Thesis, University of Liverpool, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367276.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Kannemeyer, Johan Etienne. "Artificial neural network decoding of multi-h CPM." Master's thesis, University of Cape Town, 1997. http://hdl.handle.net/11427/19638.

Full text

Abstract:

The purpose of this report is to set out the results of an investigation into the artificial neural network (ANN) decoding of multi-h continuous phase modulation (CPM) schemes. Multi-h CPM schemes offer forward error correction (FEC) capabilities for continuous transmission, digital communication systems. Multi-h CPM is reported to be a bandwidth efficient alternative to other FEC techniques such as convolutional coding, while neural networks allow for high speed decoding. A neural network decoder was found in [12], where it had been used for the decoding of a convolutional code. This neural network structure by Xiao-an Wang and Stephen 'B. Wicker implements the Viterbi Algorithm (VA). All the necessary decoding information is contained in the interconnections of the ANN, and can be found by inspection of the state trellis diagram of the convolutional code. The decoder therefore requires no training. Since all the computation is done by analogue neurons and shift registers, the neural network reduces to a hybrid digital-analogue implementation of the VA. The use of analogue neurons allows the structure to be used for high data rate communications. Furthermore, the decoder is reported to be suitable for VLSI implementation.

APA, Harvard, Vancouver, ISO, and other styles

28

Chan, Kwok Hung Billy Carleton University Dissertation Engineering Mechanical and Aerospace. "Predicting weld features using artificial neural network technology." Ottawa, 1996.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

29

Mryyan, Mahmoud. "Environmental site characterization via artificial neural network approach." Diss., Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1120.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Wu, Chung-Yu. "Predicting water table fluctuations using artificial neural network." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8826.

Full text

Abstract:

Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Fischell Dept. of Bioengineering . Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

APA, Harvard, Vancouver, ISO, and other styles

31

Dario, Ugo <1986&gt. "Forecasting energy market: an artificial neural network approach." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/5810.

Full text

Abstract:

Artificial Neural Network as a universal function approximators can be used for mapping any nonlinear function. Used in different fields of application (congnitive science, engineering, biology, finance..), ANN have become popular in finance for their power in pattern recognition, classification and forecasting. This paper specifically examines the used of ANN in the energy market in order to build a forecast price on the energy commodities. A brief study on the feature of the energy market, in particular crude oil and natural gas prices, will be followed by an implementation of an ANN system for the forecast. Finally a comparison between a real and estimated price will be done to see if the ANN could be considered a good forecasting tool also in the energy market.

APA, Harvard, Vancouver, ISO, and other styles

32

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

Full text

Abstract:

Agents being able to play board games such as Tic Tac Toe, Chess, Go and Arimaa has been, and still is, a major difficulty in Artificial Intelligence. For the mentioned board games, there is a certain amount of legal moves a player can do in a specific board state. Tic Tac Toe have in average around 4-5 legal moves, with a total amount of 255168 possible games. Both Chess, Go and Arimaa have an increased amount of possible legal moves to do, and an almost infinite amount of possible games, making it impossible to have complete knowledge of the outcome. This thesis work have created various Neural Networks, with the purpose of evaluating the likelihood of winning a game given a certain board state. An improved evaluation function would compensate for the inability of doing a deeper tree search in Arimaa, and the anticipation is to compete on equal skills against another well-performing agent (meijin) having one less search depth. The results shows great potential. From a mere one hundred games against meijin, the network manages to separate good from bad positions, and after another one hundred games able to beat meijin with equal search depth. It seems promising that by improving the training and by testing different sizes for the neural network that a neural network could win even with one less search depth. The huge branching factor of Arimaa makes such an improvement of the evaluation beneficial, even if the evaluation would be 10 000 times more slow.

APA, Harvard, Vancouver, ISO, and other styles

33

Lind, Benjamin. "Artificial Neural Networks for Image Improvement." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-137661.

Full text

Abstract:

After a digital photo has been taken by a camera, it can be manipulated to be more appealing. Two ways of doing that are to reduce noise and to increase the saturation. With time and skills in an image manipulating program, this is usually done by hand. In this thesis, automatic image improvement based on artificial neural networks is explored and evaluated qualitatively and quantitatively. A new approach, which builds on an existing method for colorizing gray scale images is presented and its performance compared both to simpler methods and the state of the art in image denoising. Saturation is lowered and noise added to original images, which the methods receive as inputs to improve upon. The new method is shown to improve in some cases but not all, depending on the image and how it was modified before given to the method.

APA, Harvard, Vancouver, ISO, and other styles

34

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

35

Tracy, Justin. "Prediction of wind speeds with an artificial neural network." Click here to view, 2010. http://digitalcommons.calpoly.edu/eesp/24/.

Full text

Abstract:

Thesis (B.S.)--California Polytechnic State University, 2010.
Project advisor: Xiao-Hua Yu. Title from PDF title page; viewed on Apr. 20, 2010. Includes bibliographical references. Also available on microfiche.

APA, Harvard, Vancouver, ISO, and other styles

36

Xu, Le Yan. "Artificial neural network short-term electrical load forecasting techniques." Thesis, University of Macau, 1999. http://umaclib3.umac.mo/record=b1445624.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Zeng, Shi-Ran, and 曾世任. "Artificial Neural Network for Image Recognition." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/87588825955623867932.

Full text

Abstract:

碩士
國立高雄海洋科技大學
電訊工程研究所
99
Biometrics is a common topic. In this field, neural network is a common machine learning algorithm, and it has been applied to many fields. Recently, support vector machines (referred to as SVM) which is based on statistical learning theory catches the most attention. It is because SVM has the better recognition capability and faster calculation speed than the general neural networks; furthermore, it does not have the situation of over-learning. There are many researches proving that SVM has good performance of recognition in the open literature. Probabilistic neural network (referred to as PNN) is a kind of neural network based on Bayesian decision theory, and it belongs to the feedforward network architecture. PNN is highly regarded due to its short training time, and also, it does not have the iterative process. In this thesis, we apply in human face recognition and Traditional Chinese handwriting recognition. Most researches use the public face databases in human face recognition. For example, they are ORL, Yale, INDIAN, etc. Thus, we use data source both from ORL and the database created by ourselves in this study. In handwriting, the database was made of 20 persons handwriting in Traditional Chinese. In this study, we consider individual handwriting habits use different quantitative methods to explore the feasibility of using handwriting recognition as an identification identity. The experimental results show that the recognition rate by using SVM and ORL is 96%, while the recognition rate for our database is 92%(Block Background) and 80%(White Background) respectively. In Traditional Chinese handwriting, the best rate of using SVM to recognize is 75%, while the best rate for PNN is 80%.

APA, Harvard, Vancouver, ISO, and other styles

38

Hwang, Yih-Shyan, and 黃議賢. "Password Authentication Using Artificial Neural Network." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/00101971263020082688.

Full text

Abstract:

碩士
國立中興大學
應用數學研究所
82
In this dissertation, a password authentication scheme based on artificial neural network model with modified perceptron algorithm and a strong cryptographic operation such as DES(data encryption standard) is proposed. Because of parallel computing characteristics of artificial neural network, the scheme can quickly and efficiently respond to any login attempt. Thus, it is suitable for real-time service. Moreover, each user is completely free to choose his own identifier and password. Because those identifiers and passwords in the system are combined together, any illegal modification by the intruder to the weight matrices in the artificial neural network will influence the others and can easily be detected. Furthermore, after a new user is inserted into the system, it needs only to add few terms of the former weight matrices. Therefore, our scheme is suitable for practical implementation.

APA, Harvard, Vancouver, ISO, and other styles

39

Su, chutin, and 蘇祝鼎. "Artificial Neural Network for Dipole Localization." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/64838032357715825842.

Full text

Abstract:

碩士
國立交通大學
控制工程系
85
In this thesis, we use the neural networks to deal with the problem of dipole localization. We aim at estimating location, orientation, and moment strength of a single dipole which induces epilepsy in human brain. The inverseproblem is a highly nonlinear approximation process. We applied current dipolemodel to generate the brain electrical potential distribution on the scalp. The dipole and its corresponding brain potentials were used as training patternsfor the neural networks. A neural network trained to learn correspondence of dipoles to brain potential distribution can be used to estimate the dipole''slocation, orientation, and moment strength. It will be useful for clinicalapplications. In this reasearch, we investigatedthe cppability of neural network in dipole localization. The performance of the neural network depends on region of dipole localization andorientation of dipole moment. we also studied the effect of noise interferencefor the performance of neural network. We found that the accuracy of dipole localization decreased as the signal-to-noise ratio was poor. In addition, we proposed a model of hybrid network . Compared with the conventional multi-layer perceptrons network, the hybri d network indeed requires less training time and achieves better localization results. It might be a feasible neural network model for dipole localization.

APA, Harvard, Vancouver, ISO, and other styles

40

Chen, You-Yu, and 陳宥諭. "DOA Estimation with Artificial Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2pxvzy.

Full text

Abstract:

碩士
國立交通大學
電信工程研究所
107
A uniform linear array can receive multiple unequal power signals coming from different direction-of-arrival (DOA). This thesis considers DOA estimation with artificial neural network (NN). Conventional NN approaches are not effective for the unequal-power scenario. Also, the computational complexity is very high. Incorporating signal processing techniques, we propose two NNs to solve the problems. The first NN divides the estimation range into sectors, and consists of a spatial filter and a classifier. With a rotation operation, a spatial filter and a classifier can be used for all sectors, significantly reducing the training time and computational complexity. The second NN uses the same sector-based processing structure. However, the spatial filter is replaced with a power detector. With a frequency-domain nulling operation, only a power detector and a classifier are needed for all sectors. The computational complexity of the second NN can be further reduced. Simulation results show that the performance of the proposed NNs can outperform the well-known MUSIC algorithm under low SNR. Also, the computational complexity can also be lower than that of MUSIC.

APA, Harvard, Vancouver, ISO, and other styles

41

SINGH, ASHISH KUMAR. "RAINFALL FORECASTING USING ARTIFICIAL NEURAL NETWORK." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15552.

Full text

Abstract:

Rainfall forecasting is the application of science and technology to predict the state of rainfall for a given location. This is done by collecting the quantitative data of the rainfall and using scientific understanding of the rainfall process to forecast the future conditions. In India, Rainfall forecasting is done by Indian Meteorological Department (IMD), New Delhi which provides the real-time monitoring and statistical analysis of district-wise daily rainfall. Several research works have been done using different methodologies of which the ANN technique is the fastest and provides reliable solutions. In this dissertation, ANN methodology is applied for forecasting of rainfall in Delhi region. Here, ANN methodology is used to forecast rainfall using various configuration of the models. This configuration depend on the various structural parameters, such as, number of hidden layers, number of neurons in each layer, activation functions and training backpropagation algorithms. These models are categorized according to the training algorithms, namely Levenberg-Marqurdt backpropagation algorithm (LM), Bayesian regularization backpropagation (BR) algorithm and Scaled Conjugate backpropagation (SC) algorithm. Seven models are there in each category. These models have been trained and tested. The results give two models with least value of performance parameter, ‘mse’, one from LM and BR each with three hidden layers with 10 number of neurons in each layer. Then, the forecast of the selected models have been checked for validation which give the satisfactory results for ANN based forecasting of rainfall in Delhi region.

APA, Harvard, Vancouver, ISO, and other styles

42

Guha, Devi Rani. "Artificial Neural Network Based Channel Equalization." Thesis, 2011. http://ethesis.nitrkl.ac.in/2081/1/devi-thesis-corrected.pdf.

Full text

Abstract:

The field of digital data communications has experienced an explosive growth in the last three decade with the growth of internet technologies, high speed and efficient data transmission over communication channel has gained significant importance. The rate of data transmissions over a communication system is limited due to the effects of linear and nonlinear distortion. Linear distortions occure in from of inter-symbol interference (ISI), co-channel interference (CCI) and adjacent channel interference (ACI) in the presence of additive white Gaussian noise. Nonlinear distortions are caused due to the subsystems like amplifiers, modulator and demodulator along with nature of the medium. Some times burst noise occurs in communication system. Different equalization techniques are used to mitigate these effects. Adaptive channel equalizers are used in digital communication systems. The equalizer located at the receiver removes the effects of ISI, CCI, burst noise interference and attempts to recover the transmitted symbols. It has been seen that linear equalizers show poor performance, where as nonlinear equalizer provide superior performance. Artificial neural network based multi layer perceptron (MLP) based equalizers have been used for equalization in the last two decade. The equalizer is a feed-forward network consists of one or more hidden nodes between its input and output layers and is trained by popular error based back propagation (BP) algorithm. However this algorithm suffers from slow convergence rate, depending on the size of network. It has been seen that an optimal equalizer based on maximum a-posterior probability (MAP) criterion can be implemented using Radial basis function (RBF) network. In a RBF equalizer, centres are fixed using K-mean clustering and weights are trained using LMS algorithm. RBF equalizer can mitigate ISI interference effectively providing minimum BER plot. But when the input order is increased the number of centre of the network increases and makes the network more complicated. A RBF network, to mitigate the effects of CCI is very complex with large number of centres. To overcome computational complexity issues, a single neuron based chebyshev neural network (ChNN) and functional link ANN (FLANN) have been proposed. These neural networks are single layer network in which the original input pattern is expanded to a higher dimensional space using nonlinear functions and have capability to provide arbitrarily complex decision regions. More recently, a rank based statistics approach known as Wilcoxon learning method has been proposed for signal processing application. The Wilcoxon learning algorithm has been applied to neural networks like Wilcoxon Multilayer Perceptron Neural Network (WMLPNN), Wilcoxon Generalized Radial Basis Function Network (WGRBF). The Wilcoxon approach provides promising methodology for many machine learning problems. This motivated us to introduce these networks in the field of channel equalization application. In this thesis we have used WMLPNN and WGRBF network to mitigate ISI, CCI and burst noise interference. It is observed that the equalizers trained with Wilcoxon learning algorithm offers improved performance in terms of convergence characteristic and bit error rate performance in comparison to gradient based training for MLP and RBF. Extensive simulation studies have been carried out to validate the proposed technique. The performance of Wilcoxon networks is better then linear equalizers trained with LMS and RLS algorithm and RBF equalizer in the case of burst noise and CCI mitigations.

APA, Harvard, Vancouver, ISO, and other styles

43

Xue, Kuo Qiang, and 薛國強. "An intelligent sales forecasting system through artificial neural networks and fuzzy neural network." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/07455980576654976365.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Lin, Tz-tsau, and 林子超. "Structural Damage Detection using Artificial Neural Network." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/61054947437243824323.

Full text

Abstract:

碩士
國立成功大學
航空太空工程學系
85
This thesis presents an approach based on the artificial neural network to detect damages in a structure. By using the modal information of a structure before and after damage, we construct an artificial neural network model.The neural network is trained by examples which simulate various cases of structural damage using the finite element model of the structural system. The objective is to detect possible damage, including both extent and location,in the structure. Validity of the proposed approach is confirmed by using simulated examples in which the effect of noise and incomplete mode are included.

APA, Harvard, Vancouver, ISO, and other styles

45

Lin, I.-Chih, and 林奕志. "Infrared Face Recognition Using Artificial Neural Network." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/70053617787203072670.

Full text

Abstract:

碩士
國立高雄第一科技大學
電腦與通訊工程所
92
This research aims at the construction of an infrared face recognition system. Although the development of the visible face recognition system has been developed for many years, as well as many other recognition algorithms, the performance is not satisfactory, due to several environmental distracters, such as light, the instruments, which bring about the problems of feature extraction. However, as far as this infrared face recognition system is concerned, based on the heat radiation of the human body, the final extracted information is the actual temperature which would not be affected by light, for example. And that is one of the advantages of the use of infrared images, which draws increasing attention for further studies. The employment of the infrared face recognition system which would not be affected by light is proven successfully. Also, the infrared image of different targets cannot be counterfeited. Hence, the performance of the infrared face recognition is regarded more effective than that of the visible face recognition system. In practice, due to the high expense of the infrared machine plus the serious noise problem and its poor resolution, the infrared face recognition system is currently under further research and development. In this thesis, the employment of a new technique in the infrared face recognition field is studied, with the comparison and contrast between the performances of the new analysis and the traditional analysis. The first part of this thesis is to introduce the methods for the image pre-process and the face clip. In the face clip, two methods to clip the two different kinds of the face images from the background are illustrated. Some adjustments are in particular made for the clipped face images as the preparation for the next discussion. Secondly, three kinds of analysis methods in the feature extraction stage are executed. Because of the resolution limits of the infrared machine, even though the temperature accuracy of the machine can achieve hundredth, there are still several problems in the facial feature extraction. In other words, the visible face recognition, used for the homely facial feature extraction, is not as practical for infrared image. With different targets, the facial features of the persons may not be successfully extracted as expected. So, as far as feature extraction is concerned, the entire face image is discussed other than the local feature extraction. The third part is the construction of the recognition system. In this thesis, the “Plastic Perceptron” to classify different people is used. In the traditional neural network, if the user wants to add the new extracted facial information into the recognition system, the entire neural network should be retrained. But now, it is not necessary when employing the Plastic Perceptron. Therefore, the Plastic Perceptron is regarded as more suitable for face recognition.

APA, Harvard, Vancouver, ISO, and other styles

46

Horng, Yu Jing, and 洪毓鈞. "Process Optimization via an Artificial Neural Network." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/61356944221656412222.

Full text

Abstract:

碩士
逢甲大學
化學工程研究所
83
In this thesis, we consider the issue of applying neural network to the process optimization problem and robust controller design problem. The objective function of unconstrainted optimization problem can be mapped on to the energy function of the artificail neural network(ANN)in a direct way, and the network will find the minimum by obeying its own dynamics. For the optimization problem with constraints, we used the augmented Lagrange multiplier method to transform problem into a problem in which a single unconstrainted function is minimized, then we found the answer by the same step. As for the robust controller design problem, we applied the idea of Rotstein et al. to formulate the robust characteristic polynomial assignment problem as an optimization problem subject to linear constraints with uncertainty, at last we attained the controller by the same methods of optimization function with constraints. In order to prove the ability of artificial neural network and the affection of parameters, we tested several examples, the result was satisfactory and robust controller was better than the result of literature.

APA, Harvard, Vancouver, ISO, and other styles

47

WANG, YOU-REN, and 王祐人. "Artificial neural network for digits pattern recognition." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/09177770124098150633.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Gau, Peng-wei, and 高鵬惟. "Image Fusion Algorithm using Artificial Neural Network." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/85802827401493351920.

Full text

Abstract:

碩士
義守大學
資訊工程學系碩士班
97
The wavelength of visible light is between 0.4~0.7μm which is also the range of most electro-optical device. However, in additional to visible lights, there are many other devices providing useful information hidden in a variety of wavelength. Among those applications, infrared information is most widely used. Infrared information essentially responds the thermal information of objects in the scene. One of its unique features is that visible sensors see nothing in the night while the infrared sensors see things such as animals and buildings, which emit “hot”. Since visible information and infrared information provide the complementary features, it is reasonable to fuse the two signals to see more things in different weather and background situations. This thesis proposes an image fusion algorithm by using artificial neural network which combines the visible and the infrared images together to obtain complementary and useful information. By adjusting appropriate fusion parameters, we can obtain the fused image with better quality.

APA, Harvard, Vancouver, ISO, and other styles

49

Wang, Chen-Kuei, and 王珍貴. "Simulating Typhoon Rainfall with Artificial Neural Network." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/68782924427622053889.

Full text

Abstract:

碩士
國立成功大學
水利及海洋工程學系碩博士班
95
Typhoon brought abundant rainfall on its covering area along moving path. The characteristics of typhoon itself and local terrain and meteorological factors of a gage station affect the amount of rainfall. It is difficult to estimate the real-time rainfall depth of typhoon because the relationship between rainfall and related factors is non-linear and interact. In this thesis, a Back-Propagation Artificial Neural Network model (BPN) was adopted to simulate and forecast rainfall. This model could imitate the complicated non-linear behavior of rainfall process of typhoon. Rainfall and typhoon data of 18 typhoons of Tsengwen gage station were collected for case study. This thesis selected the data of 14 typhoons for model calibration and 4 for validation. There were 17 factors in total for model input. They could be divided into two categories. One was typhoon’s property which included position of typhoon center, wind velocity, atmospheric pressure and radius of grade 7 wind. The other was the characteristic factors of gage station. These were local wind velocity, temperature and topographic parameters. There are two types of BPN models developed in this thesis. The model had two hidden layers to increase the ability of description. One layer had 5 processing elements and the other had 2 processing elements. By properly adjusting the values of axon weighting and neuron bias, the BPN model could memorize the physical mechanism of the process of typhoon’s rainfall. Model I selected the major factors which affect the rainfall depth through principle component analysis as the model input. Model II adopted characteristic parameter of typhoon, shield height, wind velocity and direction, temperature and atmospheric pressure, and antecedent rainfall of gage station as model input. The results showed that the model II achieved better accuracy than model I. In addition, using 3-hour as simulating time step can provide better accuracy in both rainfall pattern and peak rainfall depth than 1-hour simulation. Therefore, the BPN typhoon rainfall model proposed in this thesis could satisfactorily simulate the pattern and depth of typhoon rainfall.

APA, Harvard, Vancouver, ISO, and other styles

50

王奕鈞. "Cadastral Coordinate Transformation Using Artificial Neural Network." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/85629959425775248835.

Full text

Abstract:

碩士
國立政治大學
地政研究所
94
Currently, there are two cadastral coordinate systems used in Taiwan. They are TWD67 (Taiwan Datum 1967) and TWD97 (Taiwan Datum 1997) cadastral coordinate systems respectively. Frequently it is necessary to transform from one coordinate system to another. One of the most widely used method is Least-Squares with affine transformations. The artificial neural network (ANN) provides a new technology for cadastral coordinate transformation. The popularity of this methodology is rapidly growing. The greatest advantage of ANN is that it can be used very successfully with a huge quantity of data and free-model estimation that traditional transformation methods cannot be applied. In this research coordinate transformation between TWD67 and TWD97 with artificial neural network (ANN) and Least-Squares with affine transformations were examined. Besides, in order to overcome the so called ‘‘Black Box Problem’’ of ANN, algorithm of applying artificial neural network to develop regional grid-based cadastral coordinate transformation model was proposed. Three data sets with varied sizes from the Taiwan region are used to test the proposed algorithms. The test results show that the coordinate transformation accuracies using the ANN models are better than those of using other methods, such as, Least-Squares with affine transformations. The proposed algorithms and the detailed test results are presented in this paper.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!