Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

In the present study, strength of meta-heuristic computing techniques is exploited for estimation problem of Hammerstein controlled auto regressive auto regressive moving average (HCARARMA) system using differential evolution (DE), genetic algorithms (GAs), pattern search (PS) and simulated annealing (SA) algorithms. The approximation theory in mean squared error sense is utilized for construction of cost function for HCARARMA model and highly uncorrelated adjustable parameter of the system is optimized with global search exploration of DE, GAs, PS and SA algorithms. Comparative study is carried out from desired known parameters of the HCARARMA model for different degree of freedom and noise variation scenarios. Performance analysis of the DE, GAs, PS and SA algorithms is conducted through results of statistics based on sufficient large independent executions in terms of measure of central tendency and variation for both precision and complexity indices. The exhaustive simulations established that the population-based heuristics are more accurate than single solution-based methodologies for HCARARMA identification. (C) 2019 Elsevier B.V. All rights reserved.