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http://hdl.handle.net/2080/1046
Title: | An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identification |
Authors: | Subudhi, B Jena, D |
Keywords: | Differential evolution neural network (NN) nonlinear system identification Levenberg Marquardt algorithm |
Issue Date: | 2009 |
Publisher: | Springer |
Citation: | International Journal of Automation and Computing, Volume 6, No 2, May 2009, Pages 137-144 |
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. |
URI: | http://dx.doi.org/10.1007/s11633-009-0137-0 http://hdl.handle.net/2080/1046 |
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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