Improving The Generalization Ability Of Neural Network Classifiers

Author : Hanan Hassan Ali Adlan
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (975 download)

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Download or read book Rough Neural Networks Architecture for Improving Generalization in Pattern Recognition written by Hanan Hassan Ali Adlan and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are found to be attractive trainable machines for pattern recognition. The capability of these models to accomodate wide variety and variability of conditions, and the ability 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 architecture 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 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 approximations transform. The basic design for the network is provided together with the learning rules. The architechture 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 subsystem, reffered 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 methodology for designing nonlinear filters withour prior knowledge of the problem domain. The RNN was used to detect patterns present in satellite imgae. A novel feature extraction algorithm was developed to extract the feature vectors. The alogorithm 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 for image enviroments.