Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Neural networks are found to be attractive trainable machines for pattern recognition. The capability of these models to accommodate wide variety and variability of conditions, and the ability to imitate brain functions, make them popular research area. This research focuses on developing hybrid...
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my-upm-ir.51162013-05-27T07:20:30Z Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition 2004 Ali Adlan, Hanan Hassan Neural networks are found to be attractive trainable machines for pattern recognition. The capability of these models to accommodate wide variety and variability of conditions, and the ability to imitate brain functions, make them popular research area. This research focuses on developing hybrid rough neural networks. These novel approaches are assumed to provide superior performance with respect to detection and automatic target recognition.In this thesis, hybrid architectures of rough set theory and neural networks have been investigated, developed, and implemented. The first hybrid approach provides novel neural network referred to as Rough Shared weight Neural Networks (RSNN). It uses the concept of approximation based on rough neurons to feature extraction, and experiences the methodology of weight sharing. The network stages are a feature extraction network, and a classification network. The extraction network is composed of rough neurons that accounts for the upper and lower approximations and embeds a membership function to replace ordinary activation functions. The neural network learns the rough set’s upper and lower approximations as feature extractors simultaneously with classification. The RSNN implements a novel approximation transform. The basic design for the network is provided together with the learning rules. The architecture provides a novel method to pattern recognition and is expected to be robust to any pattern recognition problem. The second hybrid approach is a two stand alone subsystems, referred to as Rough Neural Networks (RNN). The extraction network extracts detectors that represent pattern’s classes to be supplied to the classification network. It works as a filter for original distilled features based on equivalence relations and rough set reduction, while the second is responsible for classification of the outputs from the first system. The two approaches were applied to image pattern recognition problems. The RSNN was applied to automatic target recognition problem. The data is Synthetic Aperture Radar (SAR) image scenes of tanks, and background. The RSNN provides a novel methodology for designing nonlinear filters without prior knowledge of the problem domain. The RNN was used to detect patterns present in satellite image. A novel feature extraction algorithm was developed to extract the feature vectors. The algorithm enhances the recognition ability of the system compared to manual extraction and labeling of pattern classes. The performance of the rough backpropagation network is improved compared to backpropagation of the same architecture. The network has been designed to produce detection plane for the desired pattern. The hybrid approaches developed in this thesis provide novel techniques to recognition static and dynamic representation of patterns. In both domains the rough set theory improved generalization of the neural networks paradigms. The methodologies are theoretically robust to any pattern recognition problem, and are proved practically for image environments. Neural networks 2004 Thesis http://psasir.upm.edu.my/id/eprint/5116/ http://psasir.upm.edu.my/id/eprint/5116/1/FK_2004_91.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Neural networks Faculty of Engineering English |
| institution |
Universiti Putra Malaysia |
| collection |
PSAS Institutional Repository |
| language |
English English |
| topic |
Neural networks |
| spellingShingle |
Neural networks Ali Adlan, Hanan Hassan Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition |
| description |
Neural networks are found to be attractive trainable machines for pattern recognition.
The capability of these models to accommodate wide variety and variability of
conditions, and the ability to imitate brain functions, make them popular research
area.
This research focuses on developing hybrid rough neural networks. These novel
approaches are assumed to provide superior performance with respect to detection
and automatic target recognition.In this thesis, hybrid architectures of rough set theory and neural networks have been
investigated, developed, and implemented. The first hybrid approach provides novel
neural network referred to as Rough Shared weight Neural Networks (RSNN). It uses
the concept of approximation based on rough neurons to feature extraction, and
experiences the methodology of weight sharing. The network stages are a feature
extraction network, and a classification network. The extraction network is
composed of rough neurons that accounts for the upper and lower approximations
and embeds a membership function to replace ordinary activation functions. The
neural network learns the rough set’s upper and lower approximations as feature
extractors simultaneously with classification. The RSNN implements a novel
approximation transform. The basic design for the network is provided together with
the learning rules. The architecture provides a novel method to pattern recognition
and is expected to be robust to any pattern recognition problem.
The second hybrid approach is a two stand alone subsystems, referred to as Rough
Neural Networks (RNN). The extraction network extracts detectors that represent
pattern’s classes to be supplied to the classification network. It works as a filter for
original distilled features based on equivalence relations and rough set reduction,
while the second is responsible for classification of the outputs from the first system.
The two approaches were applied to image pattern recognition problems. The RSNN
was applied to automatic target recognition problem. The data is Synthetic Aperture
Radar (SAR) image scenes of tanks, and background. The RSNN provides a novel
methodology for designing nonlinear filters without prior knowledge of the problem domain. The RNN was used to detect patterns present in satellite image. A novel
feature extraction algorithm was developed to extract the feature vectors. The
algorithm enhances the recognition ability of the system compared to manual
extraction and labeling of pattern classes. The performance of the rough
backpropagation network is improved compared to backpropagation of the same
architecture. The network has been designed to produce detection plane for the
desired pattern.
The hybrid approaches developed in this thesis provide novel techniques to
recognition static and dynamic representation of patterns. In both domains the rough
set theory improved generalization of the neural networks paradigms. The
methodologies are theoretically robust to any pattern recognition problem, and are
proved practically for image environments. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Ali Adlan, Hanan Hassan |
| author_facet |
Ali Adlan, Hanan Hassan |
| author_sort |
Ali Adlan, Hanan Hassan |
| title |
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
|
| title_short |
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
|
| title_full |
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
|
| title_fullStr |
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
|
| title_full_unstemmed |
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
|
| title_sort |
rough neural networks architecture for improving generalization in pattern recognition |
| granting_institution |
Universiti Putra Malaysia |
| granting_department |
Faculty of Engineering |
| publishDate |
2004 |
| url |
http://psasir.upm.edu.my/id/eprint/5116/1/FK_2004_91.pdf |
| _version_ |
1747810354474254336 |