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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1288

Title: A differential evolution based neural network approach to nonlinear system identification
Authors: Subudhi, B
Jena, D
Keywords: Back propagation
Differential evolution
Evolutionary computation
Nonlinear system identification
Opposition based differential evolution
Issue Date: Jan-2010
Publisher: Elsevier
Citation: Applied Soft Computing 11 (2011) 861–871
Abstract: This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work,twoevolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input–multi-output system (TRMS) to verify the identification performance.
Description: Copyright for this article belongs to Elsevier
URI: http://dx.doi.org/10.1016/j.asoc.2010.01.006
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