Fast Identification of Wiener-Hammerstein Systems using Discrete Optimization
arXiv:1804.0703412 citationsh-index: 35
Originality Synthesis-oriented
AI Analysis
For control and signal processing engineers, it offers a faster method to identify Wiener-Hammerstein systems, though the improvement is incremental.
The paper proposes a fast identification algorithm for Wiener-Hammerstein systems using discrete optimization (genetic algorithm), significantly improving computational cost while maintaining accuracy.
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.