Iterative parameter identification with initial value optimization for colored noise Hammerstein systems under...

Given the presence of significant and possibly discontinuous nonlinearities on the input side, the widespread existence of colored noise in practical environments, and the prevalent use of non-uniform sampling strategies, the parameter identification problem for non-uniformly sampled piecewise nonlinear Hammerstein processes with colored noise becomes notably complex and challenging. This study employs a gradient iterative algorithm for parameter estimation. To suppress the interference of colored noise, a data filtering technique is introduced; to accelerate the convergence process, a momentum factor is incorporated into the gradient update. To further enhance the accuracy and efficiency of parameter identification, an equilibrium optimizer is embedded into the algorithmic framework to optimize the initial parameter values, thereby proposing the equilibrium-optimizer-based filtered momentum gradient iterative algorithm. Compared to the baseline algorithm without the optimizer, the proposed algorithm significantly improves the convergence speed and effectively avoids becoming trapped in local optima. Finally, numerical simulations and a wind power system case study are conducted to verify the effectiveness and superiority of the proposed method.