Name
Title | ||
|---|---|---|
Nonlinear system modeling using a self-organizing recurrent radial basis function neural network |
Abstract | ||
|---|---|---|
•An improved LM, incorporating an adaptive learning rate strategy into the learning process, is developed to improve the modeling performance.•The structure of RRBFNN can be self-organized by using the efficient information-oriented algorithm during the learning process.•The convergence of IOA-RRBFNN has been demonstrated theoretically and experimentally. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2017.10.030 | Applied Soft Computing |
Keywords | Field | DocType |
Information-oriented algorithm,Recurrent radial basis function neural network,Nonlinear system modeling,Improved Levenberg-Marquardt algorithm | Convergence (routing),Radial basis function network,Mathematical optimization,Nonlinear system,Radial basis function neural,Computer science,Artificial intelligence,Machine learning,Computational complexity theory | Journal |
Volume | ISSN | Citations |
71 | 1568-4946 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-Gui Han | 1 | 476 | 39.06 |
| 郭亚男 | 2 | 0 | 0.34 |
| 乔俊飞 | 3 | 44 | 2.77 |
| Hong-Gui Han | 4 | 2 | 0.70 |
| Ya-Nan Guo | 5 | 0 | 0.34 |
| Jun-Fei Qiao | 6 | 2 | 0.70 |