Log in

Relevant bibliographies by topics / Graph-based neural network model

Contents

Dissertations / Theses

Academic literature on the topic 'Graph-based neural network model'

Author: Grafiati

Published: 6 September 2023

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

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Graph-based neural network model.'

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.

Dissertations / Theses on the topic "Graph-based neural network model"

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/.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

More sources

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!