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dc.contributor.authorChakraborty, B.
dc.date.accessioned2008-08-11T12:32:40Z
dc.date.available2008-08-11T12:32:40Z
dc.date.issued2002
dc.identifier.citationAdvances in soft computing - AFSS 2002. 2nd ed.. eds. by: Pal, N.R.; Sugeno, M.(2002 AFSS Int. Conf. on Fuzzy Systems; Calcutta; India; 3-6 Feb 2002). (Lecture Notes in Computer ScienceArtificial Intelligence; 2275), 245-250p.
dc.identifier.otherno
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/1449
dc.description.abstractThis paper presents a study results of the Artificial Neural Network (ANN) architectures [Self-Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used for backscatter data acquisitions from three distinctly different seafloor's from the Arabian Sea. With some preprocessing of the snapshots, the performance of the SOM network is observed to be quite good. For unsupervised SOM network, only single snapshot is used for the training, and number of snapshots for subsequent testing of the network. Feature selection from ASCII data is an important component for a supervised MLP based network. Four selected features are used for training the network. The test results of the MLP based network are also discussed in the text.
dc.language.isoen
dc.publisherSpringer-Verlag; Berlin, Heidelberg
dc.rightsSpringer
dc.subjectocean floor
dc.subjectechosounders
dc.subjectbackscatter
dc.subjectdata acquisition
dc.subjectcontinental shelves
dc.titleA neural network based seafloor classification using acoustic backscatter
dc.typeConference Article


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