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dc.contributor.advisorLivesey, Mike
dc.contributor.authorBairaktaris, Dmitrios
dc.coverage.spatial[9], 138 p.en_US
dc.date.accessioned2016-08-04T16:09:24Z
dc.date.available2016-08-04T16:09:24Z
dc.date.issued1991
dc.identifieruk.bl.ethos.314704
dc.identifier.urihttps://hdl.handle.net/10023/9261
dc.description.abstractThis thesis introduces and explores the notion of a real-world environment with respect to adaptive pattern recognition and neural network systems. It then examines the individual properties of a real-world environment and proposes Continuous Adaptation, Persistence of information and Context-sensitive recognition to be the major design criteria a neural network system in a real-world environment should satisfy. Based on these criteria, it then assesses the performance of Hopfield networks and Associative Memory systems and identifies their operational limitations. This leads to the introduction of Randomized Internal Representations, a novel class of neural network systems which stores information in a fully distributed way yet is capable of encoding and utilizing context. It then assesses the performance of Competitive Learning and Adaptive Resonance Theory systems and again having identified their operational weakness, it describes the Dynamic Adaptation Scheme which satisfies all three design criteria for a real-world environment.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.subject.lccQ335.B2
dc.subject.lcshPattern recognition systems.en_US
dc.subject.lcshNeural networks (Computer science)en_US
dc.titleAdaptive pattern recognition in a real-world environmenten_US
dc.typeThesisen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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