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dc.contributor.authorHuang, KYen_US
dc.contributor.authorChao, YHen_US
dc.date.accessioned2014-12-08T15:25:46Z-
dc.date.available2014-12-08T15:25:46Z-
dc.date.issued2004en_US
dc.identifier.isbn0-7803-8359-1en_US
dc.identifier.issn1098-7576en_US
dc.identifier.urihttp://hdl.handle.net/11536/18206-
dc.description.abstractWe combine neural network and syntactic pattern recognition, and propose a tree automaton system for the recognition of structural seismic patterns in a seismogram Multilayer perceptron of the neural network is used for the identification of subpatterns, then a tree representation of the structural seismic pattern is constructed. We use three kinds of modified bottom-up structure preserved error correcting tree automata to recognize the tree representation of syntactic pattern, and propose a new top-down error correcting tree automaton to recognize non-structural preserved seismic pattern. In the experiments, the system is applied to the simulated and the real seismic bright spot patterns. The recognition result can improve seismic interpretation.en_US
dc.language.isoen_USen_US
dc.titleNeural network and tree automaton for seismic pattern recognitionen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGSen_US
dc.citation.spage663en_US
dc.citation.epage668en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000224941900116-
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