Abstract
SummaryIn this paper, brain storm optimization (BSO)‐based efficient identification approach has been applied to different types of stable and practically useful Nonlinear Auto Regressive Moving Average with exogenous noise (NARMAX) Hammerstein models with various performance criteria‐based assessments. Different performance measures of the estimation process like accuracy, precision and consistency have been established to ensure the general applicability and practical usefulness of the proposed approach. The accuracy and the precision of the parameter estimation are established with the corresponding bias and variance information, while the consistency has been justified with the help of hypothesis test results. BSO‐based optimum values of the output mean square errors and the parameters and their corresponding convergences ensure the stability and robustness of the proposed identification scheme. The comparative studies of the performance of the BSO algorithm with the other basic evolutionary algorithms have been reported with optimum values of the mean square errors, estimated values of the parameters, corresponding computational times and hypothesis test outcomes. Copyright © 2016 John Wiley & Sons, Ltd.
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More From: International Journal of Adaptive Control and Signal Processing
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