Subudhi, B.Jena, D.2026-02-052011Applied Soft Computing, 2011, 11, 1, pp. 861-87115684946https://doi.org/10.1016/j.asoc.2010.01.006https://idr.nitk.ac.in/handle/123456789/27364This 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, two evolutionary 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. © 2010 Elsevier B.V. All rights reserved.Differential evolutionEvolutionary computationsEvolutionary computingHighly nonlinearLevenberg-Marquardt algorithmModel identificationMulti-input multi-output systemNonlinear system identificationOne degree of freedom (1-DOF)System identificationsTwin-rotorsBackpropagationBiologyCalculationsEvolutionary algorithmsIdentification (control systems)Nonlinear systemsOrdinary differential equationsSoft computingNeural networks