Name
Abstract | ||
|---|---|---|
In this paper, we propose a new recurrent fuzzy neural networks, which has the standard state space form, we call it state-space recurrent neural networks. Input-to-state stability is applied to access robust training algorithms for system identification. Stable learning algorithms for the premise part and the consequence part of fuzzy rules are proved. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/CDC.2004.1429617 | Decision and Control, 2004. CDC. 43rd IEEE Conference |
Keywords | DocType | Volume |
fuzzy neural nets,identification,learning (artificial intelligence),recurrent neural nets,state-space methods,fuzzy rules,input-to-state stability,robust training algorithms,stable learning algorithms,state-space recurrent fuzzy neural networks,system identification,fuzzy neural network,learning artificial intelligence,recurrent neural network,state space | Conference | 5 |
ISSN | ISBN | Citations |
0191-2216 | 0-7803-8682-5 | 2 |
PageRank | References | Authors |
0.41 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Yu | 1 | 283 | 22.70 |
| Ferreyra, A. | 2 | 2 | 0.41 |