Design of momentum LMS adaptive strategy for parameter estimation of Hammerstein controlled autoregressive systems

In the present work, strength of momentum least mean square (MLMS) algorithm is exploited for nonlinear system identification problems represented with Hammerstein model. The MLMS algorithm uses the previous gradient information to estimate the current weights instead of using only current value of gradient; thus, it is faster in the convergence and less probable to trap in local minima. The perfection of the design scheme is certified through effective parameter estimation of nonlinear Hammerstein control autoregressive models. The robustness of the scheme is established by examining the performance for different levels of noise variance. The performance comparison through mean square error and Nash-Sutcliffe efficiency parameters, calculated for sufficiently large number of multiple runs, proves the intrinsic worth of MLMS algorithm in system identification.