The present work deals with the nonlinear multiple input multiple output (MIMO) system identification exploring the use of evolutionary computing techniques such as Differential Evolution.The conventionally used standard derivative based identification schemes does not work satisfactorily for nonlinear MIMO systems, which is due to premature settling of weights but the proposed update algorithm works better preventing the premature settling of the model parameters. Simultaneously, the performance comparison of different variants of DE has been demonstrated which reveals the best mutant of DE family that can be implemented into prescribed identification process through the real world applications.IndexTerms-MIMO, nonlinear system identification, differential evolution, mutation, crossover, variants of DE.
The paper investigates on the use of Differential Evolution (DE) for training the system identification model particularly when the measurement data are available at different sensor nodes. Under such situation the conventional DE algorithms cannot be applied directly. Hence in this paper two distributed learning algorithms known as incremental DE (IDE) and diffusion DE (DDE) have been developed to meet the requirements. The identification of nonlinear plants under different noise conditions has been obtained through simulation study and the results have been compared with distributed PSO algorithms. The performance of the proposed algorithms in terms of convergence rate and minimum mean squared error indicate that the distributed DE algorithms exhibit superior performance compared to its PSO counter parts.
Conventional error based cost function provides unsatisfactory weight update of an adaptive system when outliers are present in the training signal. To alleviate this problem in this paper a hybrid approach using differential evolution (DE) and Wilcoxon norm is proposed to provide robust training in identification of complex nonlinear systems. Exhaustive simulation study shows superior performance of the new method compared to the conventional square error based minimization method.
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