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dc.contributor.authorErichsen Junior, Rubempt_BR
dc.contributor.authorTheumann, Walter Karlpt_BR
dc.date.accessioned2014-09-23T02:12:35Zpt_BR
dc.date.issued1999pt_BR
dc.identifier.issn1063-651Xpt_BR
dc.identifier.urihttp://hdl.handle.net/10183/103647pt_BR
dc.description.abstractThe principle of adaptation in a noisy retrieval environment is extended here to a diluted attractor neural network of Q-state neurons trained with noisy data. The network is adapted to an appropriate noisy training overlap and training activity, which are determined self-consistently by the optimized retrieval attractor overlap and activity. The optimized storage capacity and the corresponding retriever overlap are considerably enhanced by an adequate threshold in the states. Explicit results for improved optimal performance and new retriever phase diagrams are obtained for Q=3 and Q=4, with coexisting phases over a wide range of thresholds. Most of the interesting results are stable to replica-symmetry-breaking fluctuations.en
dc.format.mimetypeapplication/pdf
dc.language.isoengpt_BR
dc.relation.ispartofPhysical Review. E, Statistical physics, plasmas, fluids and related interdisciplinary topics. New York. Vol. 59, no. 1 (Jan. 1999), p. 947-955pt_BR
dc.rightsOpen Accessen
dc.subjectFísica estatísticapt_BR
dc.subjectRedes neuraispt_BR
dc.subjectBiofísicapt_BR
dc.subjectTransformacoes de ordem-desordempt_BR
dc.titleOptimally adapted multistate neural networks trained with noisept_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb000235925pt_BR
dc.type.originEstrangeiropt_BR


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