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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Aibinu, Abiodun M. | - |
dc.contributor.author | Salami, Momoh-Jimoh E. | - |
dc.contributor.author | Shafie, Amir A. | - |
dc.date.accessioned | 2019-10-24T10:38:24Z | - |
dc.date.available | 2019-10-24T10:38:24Z | - |
dc.date.issued | 2011-08-01 | - |
dc.identifier.citation | Aibinu, 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.uri | https://doi.org/10.1016/j.eswa.2010.11.005 | - |
dc.identifier.uri | http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/596 | - |
dc.description.abstract | In 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.language.iso | en | en_US |
dc.publisher | Pergamon | en_US |
dc.subject | Autoregressive model | en_US |
dc.subject | Complex-valued data (CVD) | en_US |
dc.subject | Complex-valued neural network (CVNN) | en_US |
dc.subject | Diabetes | en_US |
dc.subject | Parametric models | en_US |
dc.title | A novel signal diagnosis technique using pseudo complex-valued autoregressive technique | en_US |
dc.type | Article | en_US |
Appears in Collections: | Research Articles |
Files in This Item:
File | Description | Size | Format | |
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A novel signal diagnosis technique using pseudo complex_valued autoregressive technique.pdf | Abstract | 280.57 kB | Adobe PDF | View/Open |
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