Fast Identification of Wiener-Hammerstein Systems using Discrete Optimization

TL;DR

This paper introduces a rapid identification method for Wiener-Hammerstein systems that leverages discrete optimization via genetic algorithms to efficiently separate system components, demonstrating improved speed and accuracy.

Contribution

It presents a novel, fast identification algorithm for Wiener-Hammerstein systems using genetic algorithms for discrete optimization.

Findings

Significant reduction in computational cost

High accuracy in system component separation

Validated effectiveness through numerical experiments

Abstract

This letter proposes a fast identification algorithm for Wiener-Hammerstein systems. The computational cost of separating the front and the back linear time invariant block dynamics is significantly improved by using discrete optimization. The discrete optimization is implemented as a genetic algorithm. Numerical results confirm the efficiency and accuracy of the proposed approach.