Vision-based neural network classifiers and their applications

2.50
Hdl Handle:
http://hdl.handle.net/10547/312055
Title:
Vision-based neural network classifiers and their applications
Authors:
Li, Mengxin
Abstract:
Visual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research. This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL). Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel.
Citation:
Li, M. (2005) 'Vision-based neural network classifiers and their applications'. PhD thesis. University of Luton.
Publisher:
University of Bedfordshire
Issue Date:
Sep-2005
URI:
http://hdl.handle.net/10547/312055
Type:
Thesis or dissertation
Language:
en
Description:
A thesis submitted for the degree of Doctor of Philosophy of University of Luton
Appears in Collections:
PhD e-theses

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Mengxinen
dc.date.accessioned2014-01-30T10:59:52Z-
dc.date.available2014-01-30T10:59:52Z-
dc.date.issued2005-09-
dc.identifier.citationLi, M. (2005) 'Vision-based neural network classifiers and their applications'. PhD thesis. University of Luton.en
dc.identifier.urihttp://hdl.handle.net/10547/312055-
dc.descriptionA thesis submitted for the degree of Doctor of Philosophy of University of Lutonen
dc.description.abstractVisual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research. This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL). Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel.en
dc.language.isoenen
dc.publisherUniversity of Bedfordshireen
dc.subjectG730 Neural Computingen
dc.subjectG740 Computer Visionen
dc.subjectneural networksen
dc.subjectfuzzy inputen
dc.subjectquality testingen
dc.titleVision-based neural network classifiers and their applicationsen
dc.typeThesis or dissertationen
dc.type.qualificationnamePhDen_GB
dc.type.qualificationlevelPhDen
dc.publisher.institutionUniversity of Bedfordshireen
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