Parameter estimation for Hammerstein control autoregressive systems using differential evolution

In the present study, strength of stochastic computational paradigms is investigated for parameter estimation of Hammerstein control autoregressive (HCAR) model by exploiting differential evolution, genetic algorithms and pattern search methods. Multidimensional and nonlinear nature of the problem emerging in digital signal systems along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of stochastic solvers to ensure convergence and avoid trapping in local minima. The performance of meta-heuristic approaches is validated through statistical performance indices based on absolute error, weight deviations and mean squared error. Comparative studies of HCAR system identification established efficacy of the designed methodology based on differential evolution over its counterparts.