Identification of Hammerstein model using bacteria foraging optimization algorithm

This paper presents an efficient approach for identification of a nonlinear Hammerstein model using Bacteria Foraging Optimization (BFO) Algorithm. The accuracy and the efficiency of the proposed BFO based identification scheme have been justified with the optimal value of MSE and the corresponding comparative statistical information. The statistical information of the MSE has also been provided to justify consistency of the BFO algorithm for identification of the Hammerstein model. The estimated parameters along with their corresponding deviations and convergences are shown to justify efficiency of the proposed identification strategy. The deviations of the estimated parameters from their actual values are also reported to justify precision and effectiveness of the BFO based identification approach.