Identification of a two stage cascaded nonlinear system of trigonometric nonlinearity using Particle Swarm Optimization with Aging Leader and Challengers

This paper proposes an accurate and efficient approach for identification of a two stage cascaded Hammerstein model using Particle Swarm Optimization with Aging Leader and Challengers (ALC-PSO) algorithm. To enhance the computational speed and to avoid the premature convergence criteria, ALC-PSO is used. The accuracy and the precision of the proposed ALC-PSO based identification scheme have been justified with the achieved optimal value of MSE along with its comparative study with the other reported earlier approaches. The statistical information of the MSE has also been provided to justify consistency of the ALC-PSO algorithm for identification of Hammerstein model. The estimated parameters along with their corresponding deviations and convergences are shown to justify efficiency of the proposed identification strategy. Proper identification of the linear counterpart of the considered model justifies the stability of the overall system.