Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/2490
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Abderrazak Benchabane, Abdelhak Bennia, and Fella Charif | - |
dc.date.accessioned | 2013-12-19T11:34:09Z | - |
dc.date.available | 2013-12-19T11:34:09Z | - |
dc.date.issued | 2013-12-19 | - |
dc.identifier.issn | waf | - |
dc.identifier.uri | http://hdl.handle.net/123456789/2490 | - |
dc.description | The INTERNATIONAL CONFERENCE ON ELECTRONICS & OIL: FROM THEORY TO APPLICATIONS March 05-06, 2013, Ouargla, Algeria | en_US |
dc.description.abstract | In this paper, we present an Auto-Regressive (AR) spectral estimator using a special kind of recurrent neural network proposed by Zhang called Continuous-time Zhang Neural Network (CZNN) to solve a system of linear equations. This neural network is characterized by an implicit dynamics and designed by defining a vector-valued error function instead of the usual scalar-valued norm-based error function used in the Gradient based Neural Networks (GNN). The output of the CZNN is the estimated AR coefficients so that the spectrum of the signal can be directly obtained in terms of the AR coefficients. For comparative purposes, the GNN model is also employed for AR parameters estimation. | en_US |
dc.language.iso | en | en_US |
dc.subject | Spectral Estimation | en_US |
dc.subject | Zhang neural network | en_US |
dc.subject | based neural network | en_US |
dc.subject | Gradient | en_US |
dc.subject | AR model | en_US |
dc.title | Continuous-time Zhang Neural Networks for AR Spectral Estimator | en_US |
dc.type | Article | en_US |
Appears in Collections: | 3. Faculté des sciences appliquées |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Abderrazak_Benchabane.pdf | 175,14 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.