Hierarchical gradient-based identification of multivariable discrete-time systems

By Feng Ding, Tongwen Chen. at - Automatisierungstechnik, 2005

355 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/7a63d5e3ff4cc3b6450c2c0d50107b08bcdd1619

How researchers see this paper

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

In Artificial Intelligence

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

In Civil and Structural Engineering

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

In Control and Systems Engineering

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

Citations in the corpus

• Gradient based iterative algorithms for solving a class of matrix equations (2005, methodology): “In this note, the problem will be tackled in a new way—by applying the so-called hierarchical identification principle [6], [7].”
• Gradient-Based Identification Methods for Hammerstein Nonlinear ARMAX Models (2006, methodology): “Like in [20–22], the following is to prove this theorem by using the martingale convergence theorem.”
• A GRADIENT BASED ADAPTIVE CONTROL ALGORITHM FOR DUAL‐RATE SYSTEMS (2006, background): “Notice that the number of model parameters will be increased after the polynomial transform; this will lead to increased computational complexity, which may be reduced by using the hierarchical identification scheme [13,24-27].”
• On Iterative Solutions of General Coupled Matrix Equations (2006, methodology): “According to the hierarchical identification principle [11, 12], system (6) is decomposed into two subsystems and then, based on the gradient search principle, the parameters of each subsystem are identified.”
• Newton iterative identification for a class of output nonlinear systems with moving average noises (2013, methodology): “On the basis of the work in [14, 29, 30], this paper derives two Newton iterative identification algorithms for a class of output nonlinear systems by using the Newton method and the decomposition based hierarchical identification principle [31–36].”
• The general coupled matrix equations over generalized bisymmetric matrices (2010, methodology): “The gradient-based iterative (GI) algorithms [15,20] and least squares based iterative algorithm [16] for solving (coupled) matrix equations are innovational and computationally efficient numerical algorithms and were presented based on the hierarchical identification principle [17,18] which regards”
• Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle (2008, background): “Based on the hierarchical identification principle [2–5,7,8], the unknown variable X is replaced with its estimate at time (k 1).”
• On the generalized reflexive and anti-reflexive solutions to a system of matrix equations (2012, methodology): “The HGI algorithms [16,21] and HLSI algorithms [20,21,18] for solving general (coupled) matrix equations are innovational and computationally efficient numerical methods and were proposed based on the hierarchical identification principle [17,19] which regards the unknown matrix as the system parame”