Identification methods for Hammerstein nonlinear systems

By Feng Ding, Peter Xiaoping Liu, Guangjun Liu. Digit. Signal Process., 2011

343 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Mechanics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/af3467ad06021ee96e49e302c8b2eba94e8c5476

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Yongsong Xiao
2. Junxia Ma
3. Feng Ding
4. Masoud Hajarian
5. Tongwen Chen
6. Mehdi Dehghan

In Computational Mechanics

1. Junxia Ma
2. Yongsong Xiao
3. Feng Ding

In Control and Systems Engineering

1. Junxia Ma
2. Yongsong Xiao
3. Feng Ding
4. Mehdi Dehghan
5. Masoud Hajarian
6. Tongwen Chen
7. Peter Xiaoping Liu
8. Jie Ding

Citations in the corpus

• Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling (2013, methodology): “Some work assume that the linear dynamic block is a finite impulse response (FIR) model [15], an ARX model [13,16], an ARMAX model [12], or an output error model [17].”
• Newton iterative identification for a class of output nonlinear systems with moving average noises (2013, methodology): “For identifiability, we make the assumption that ‖c‖ = 1 [41].”