Hierarchical least squares identification methods for multivariable systems

By Feng Ding, Tongwen Chen. IEEE Transactions on Automatic Control, 2005

334 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/04b9602e4aaa00e72280ac45e8504fdd7dfebb28

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Citations in the corpus

Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling (2013, methodology): “The solution is based on the hierarchical identification principle [33–35] or the Jacobi iterative technique [36].”
On the generalized bisymmetric and skew-symmetric solutions of the system of generalized Sylvester matrix equations (2011, methodology): “The HGI algorithms [11,16] and HLSI algorithms [13,15,16] for solving general (coupled) matrix equations are innovative and computationally efficient numerical algorithms and were proposed based on the hierarchical identification principle [12,14] which regards the unknown matrix as the system param”
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”
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 generalized centro‐symmetric and least squares generalized centro‐symmetric solutions of the matrix equation AYB + CYTD = E (2011, methodology): “The HGI algorithms [34, 35] and HLSI algorithms [33, 35, 36] for solving general (coupled) matrix equations are innovational and computationally efficient numerical methods and were proposed based on the hierarchical identification principle [32, 37] which regards the unknown matrix as the system pa”