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Related Experiment Videos

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

Hongchen Qin¹, Yan Ji¹

¹College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, PR China.

ISA Transactions

|June 3, 2026

Summary

This summary is machine-generated.

This study introduces an advanced gradient iterative algorithm for parameter identification in complex Hammerstein processes. The method enhances accuracy and speed by filtering colored noise and optimizing initial parameters, proving effective in simulations and a wind power case.

Keywords:

Equilibrium optimizer Filtering technique Gradient iterative algorithm Hammerstein system Non-uniform sampling Parameter estimation

Related Experiment Videos

Area of Science:

Control Systems Engineering
Signal Processing
Nonlinear System Identification

Background:

Parameter identification for non-uniformly sampled piecewise nonlinear Hammerstein processes is challenging due to nonlinearities, colored noise, and non-uniform sampling.
Existing methods struggle with convergence speed and local optima in complex noise environments.

Purpose of the Study:

To develop an efficient and accurate parameter identification algorithm for non-uniformly sampled nonlinear Hammerstein processes with colored noise.
To improve convergence speed and avoid local optima in parameter estimation.

Main Methods:

A gradient iterative algorithm is employed for parameter estimation.
Data filtering is introduced to suppress colored noise interference.
A momentum factor accelerates gradient updates.
An equilibrium optimizer is integrated to optimize initial parameter values, forming the equilibrium-optimizer-based filtered momentum gradient iterative algorithm.

Main Results:

The proposed algorithm significantly improves convergence speed compared to baseline methods.
The equilibrium optimizer effectively avoids trapping in local optima.
Numerical simulations and a wind power system case study demonstrate the method's effectiveness and superiority.

Conclusions:

The equilibrium-optimizer-based filtered momentum gradient iterative algorithm offers a robust solution for parameter identification in complex nonlinear systems.
The method provides enhanced accuracy and efficiency, outperforming traditional approaches.