Nonlinear System Identification using Evolutionary Computing based Training Schemes

The present work deals with application of recently developed evolutionary computing based training methods for non-linear system identification problem. Generally, most of the systems are nonlinear in nature. The conventionally used standard derivative based identification scheme does not work satisfactorily for nonlinear systems, which is due to premature settling of the model parameters. To prevent the premature settling of the weights, evolutionary computing based update algorithms have been proposed. In this paper we have compared three popular derivative free evolutionary computing based update algorithms namely Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) for identification of nonlinear systems, in terms of convergence graph of cost function over number of iterations. It has been demonstrated that the derivative free population based schemes provided excellent performance for identification of nonlinear systems and they are not trapped in problem of local minima as well.

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