A novel signal diagnosis technique using pseudo complex-valued autoregressive technique

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dc.contributor.authorAibinu, Abiodun M.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.contributor.authorShafie, Amir A.
dc.date.accessioned2019-10-24T10:38:24Z
dc.date.available2019-10-24T10:38:24Z
dc.date.issued2011-08-01
dc.description.abstractIn this paper, a new method of biomedical signal classification using complex- valued pseudo autoregressive (CAR) modeling approach has been proposed. The CAR coefficients were computed from the synaptic weights and coefficients of a split weight and activation function of a feedforward multilayer complex valued neural network. The performance of the proposed technique has been evaluated using PIMA Indian diabetes dataset with different complex-valued data normalization techniques and four different values of learning rate. An accuracy value of 81.28% has been obtained using this proposed technique.en_US
dc.identifier.citationAibinu, A. M., Salami, M. J. E., & Shafie, A. A. (2011). A novel signal diagnosis technique using pseudo complex-valued autoregressive technique. Expert Systems with Applications, 38(8), 9063-9069.en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2010.11.005
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/596
dc.language.isoenen_US
dc.publisherPergamonen_US
dc.subjectAutoregressive modelen_US
dc.subjectComplex-valued data (CVD)en_US
dc.subjectComplex-valued neural network (CVNN)en_US
dc.subjectDiabetesen_US
dc.subjectParametric modelsen_US
dc.titleA novel signal diagnosis technique using pseudo complex-valued autoregressive techniqueen_US
dc.typeArticleen_US
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