Reduced-order modeling methods via bivariate discrete orthogonal polynomials for two-dimensional discrete state-delayed systems-Reference-Cited by-同舟云学术

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Reduced-order modeling methods via bivariate discrete orthogonal polynomials for two-dimensional discrete state-delayed systems

Published:2023-01-07 Issue:1 Volume:34 Page:227-248
ISSN:0923-6082
Container-title:Multidimensional Systems and Signal Processing
language:en
Short-container-title:Multidim Syst Sign Process

Author:

Wang Zhao-Hong,Jiang Yao-Lin,Xu Kang-Li^ORCID

Funder

National Natural Science Foundation of China

Aviation Science Foundation Project

Graduate Innovation Project of Xinjiang Uyghur Autonomous Region

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing,Software

Link

https://link.springer.com/content/pdf/10.1007/s11045-022-00864-6.pdf

Reference44 articles.

1. Antoulas, A. C. (2005). Approximation of large-scale dynamical systems. Philadelphia: SIAM.

2. Badie, K., Alfidi, M., & Chalh, Z. (2020). Further results on $$H_{\infty }$$ filtering for uncertain $$2$$-D discrete systems. Multidimensional Systems and Signal Processing, 31(4), 1469–1490.

3. Ding, D. W., Du, X., & Li, X. L. (2015). Finite-frequency model reduction of two-dimensional digital filters. IEEE Transactions on Automatic Control, 66(6), 1624–1629.

4. Ding, F., & Chen, T. W. (2006). On iterative solutions of general coupled matrix equations. SIAM Journal on Control and Optimization, 44(6), 2269–2284.

5. Ding, F., Liu, P. X., & Ding, J. (2008). Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle. Applied Mathematics and Computation, 197(1), 41–50.

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