A recurrent neural network for solving Sylvester equation with time-varying coefficients

By Yunong Zhang, D. Jiang, Jun Wang. IEEE Trans. Neural Networks, 2002

533 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a580ea430528182eaaf29e06650eeb6b3b8ff2d9

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. Shuzhi Sam Ge
2. Yunong Zhang
3. Shaocheng Tong
4. Yong-ming Li
5. Mou Chen
6. Yanjun Liu
7. Tie-shan Li
8. Chun Lung Philip Chen

In Control and Systems Engineering

1. Shuzhi Sam Ge
2. Zhuo-Heng He
3. Yunong Zhang
4. Shaocheng Tong
5. Wei He
6. Yong-ming Li
7. Mou Chen
8. Xu Jin

In Statistical and Nonlinear Physics

1. Shuzhi Sam Ge
2. Zhuo-Heng He
3. Yunong Zhang
4. Her-Terng Yau
5. Peter C. B. Phillips

Citations in the corpus

• Design and analysis of a general recurrent neural network model for time-varying matrix inversion (2005, methodology): “For comparison with traditional neural approaches, the gradient-based neural network [17], [21], , is also simulated for”
• The general $\phi$-Hermitian solution to mixed pairs of quaternion matrix Sylvester equations (2017, background): “Sylvester-type equation is one of the important equations in system and control theory and has a huge amount of practical applications in neural network [47], robust control [36], output feedback control [31], the almost noninteracting control ([37], [42]), graph theory [6], and so on.”