Show simple item record

dc.contributor.authorLing, Hongen
dc.date.accessioned2008-02-26T03:20:35Z
dc.date.issued2007en
dc.identifier.urihttps://hdl.handle.net/10182/308
dc.description.abstractArtificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biological systems on the basis of mimicking the information processing methods in the human brain. The capability of current ANNs only focuses on approximating arbitrary deterministic input-output mappings. However, these ANNs do not adequately represent the variability which is observed in the systems' natural settings as well as capture the complexity of the whole system behaviour. This thesis addresses the development of a new class of neural networks called Stochastic Neural Networks (SNNs) in order to simulate internal stochastic properties of systems. Developing a suitable mathematical model for SNNs is based on canonical representation of stochastic processes or systems by means of Karhunen-Loéve Theorem. Some successful real examples, such as analysis of full displacement field of wood in compression, confirm the validity of the proposed neural networks. Furthermore, analysis of internal workings of SNNs provides an in-depth view on the operation of SNNs that help to gain a better understanding of the simulation of stochastic processes by SNNs.en
dc.format.extent1-100en
dc.language.isoenen
dc.publisherLincoln Universityen
dc.subjectDigital Image Correlation (DIC)en
dc.subjectapproximate identity neural networksen
dc.subjectKarhunen-Loève theoremen
dc.subjectstochastic neural networksen
dc.subjectstochastic processesen
dc.subjectwhite noiseen
dc.subjectwood structureen
dc.subjectwood displacementen
dc.titleImplementation of stochastic neural networks for approximating random processesen
dc.typeThesis
thesis.degree.grantorLincoln Universityen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Applied Computingen
dc.subject.marsdenFields of Research::280000 Information, Computing and Communication Sciences::280200 Artificial Intelligence and Signal and Image Processing::280212 Neural networks, genetic algorithms and fuzzy logicen
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitDepartment of Environmental Managementen
pubs.organisational-group/LU
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/DEM
pubs.publication-statusPublisheden
dc.publisher.placeChristchurchen


Files in this item

Default Thumbnail

This item appears in the following Collection(s)

Show simple item record