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http://hdl.handle.net/2080/1046
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DC Field | Value | Language |
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dc.contributor.author | Subudhi, B | - |
dc.contributor.author | Jena, D | - |
dc.date.accessioned | 2009-09-14T15:48:14Z | - |
dc.date.available | 2009-09-14T15:48:14Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | International Journal of Automation and Computing, Volume 6, No 2, May 2009, Pages 137-144 | en |
dc.identifier.uri | http://dx.doi.org/10.1007/s11633-009-0137-0 | - |
dc.identifier.uri | http://hdl.handle.net/2080/1046 | - |
dc.description.abstract | This paper presents an improved nonlinear system identi¯cation scheme using di®erential evolution (DE), neural network (NN) and Levenberg Marquardt algorithm (LM). With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the e±cacy of the proposed improved system identi¯cation algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identi¯cation methods, namely NN and DE+NN on a number of examples including a practical case study. The identi¯cation results obtained through a series of simulation studies of these methods on di®erent nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identi¯cation can yield better identi¯cation results in terms of time of convergence and less identi¯cation error. | en |
dc.format.extent | 3303712 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | Springer | en |
dc.subject | Differential evolution | en |
dc.subject | neural network (NN) | en |
dc.subject | nonlinear system identification | en |
dc.subject | Levenberg Marquardt algorithm | en |
dc.title | An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identification | en |
dc.type | Article | en |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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ijac-bs-dj[1].pdf | 3.23 MB | Adobe PDF | View/Open |
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