Name
Title | ||
|---|---|---|
Noise-Tolerant ZNN Models for Solving Time-Varying Zero-Finding Problems: A Control-Theoretic Approach. |
Abstract | ||
|---|---|---|
This technical note proposes a noise-tolerant zeroing neural network (NTZNN) design formula, and shows how recurrent (and recursive) methods for solving time-varying problems can be designed from the viewpoint of control. The NTZNN design formula provides a control-theoretic framework to deal with the convergence, stability and robustness issues of continuous-time (and discrete-time) models. NTZNN models derived from the proposed design formula demonstrate their advantages when applied to solving time-varying zero-finding problems in the presence of noises. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TAC.2016.2566880 | IEEE Trans. Automat. Contr. |
Keywords | Field | DocType |
Mathematical model,Control theory,Numerical models,Computational modeling,Neural networks,Convergence,Robustness | Convergence (routing),Mathematical optimization,Technical note,Computer science,Robustness (computer science),Artificial intelligence,Artificial neural network,Numerical analysis,Recursion | Journal |
Volume | Issue | ISSN |
62 | 2 | 0018-9286 |
Citations | PageRank | References |
27 | 0.77 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long Jin | 1 | 537 | 28.68 |
| Yunong Zhang | 2 | 2344 | 162.43 |
| Shuai Li | 3 | 1278 | 82.46 |
| Yinyan Zhang | 4 | 67 | 2.43 |