Name
Title | ||
|---|---|---|
Noise-Tolerant and Finite-Time Convergent ZNN Models for Dynamic Matrix Moore-Penrose Inversion. |
Abstract | ||
|---|---|---|
Dynamic (or say, time-varying) problems have been a hot spot of research recently. As a general form of matrix inverse, dynamic Moore-Penrose inverse solving has received more and more attention owing to its broad applications. The approaches based on neural networks have become a popular solution to various dynamic matrix-related problems including dynamic Moore-Penrose inverse. However, existing... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TII.2019.2929055 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Numerical models,Convergence,Transmission line matrix methods,Informatics,Robots,Mathematical model,Neural networks | Applied mathematics,Inversion (meteorology),Matrix (mathematics),Computer science,Control engineering,Finite time | Journal |
Volume | Issue | ISSN |
16 | 3 | 1551-3203 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiguo Tan | 1 | 56 | 4.40 |
| Lin Xiao | 2 | 94 | 15.07 |
| Siyuan Chen | 3 | 5 | 1.43 |
| Xuanjiao Lv | 4 | 45 | 3.70 |