Name
Title | ||
|---|---|---|
Nonlinear System Modeling Using The Takagi-Sugeno Fuzzy Model And Long-Short Term Memory Cells |
Abstract | ||
|---|---|---|
The data driven black-box or gray-box models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we combine the Takagi-Sugeno fuzzy model with long-short term memory cells to overcome these disadvantages. This novel model takes the advantages of the interpretability of the fuzzy system and the good approximation ability of the long-short term memory cell. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in order to observe the advantages of the proposal. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3233/JIFS-200491 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | DocType | Volume |
LSTM, fuzzy neural networks, nonlinear system identification | Journal | 39 |
Issue | ISSN | Citations |
3 | 1064-1246 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Yu | 1 | 246 | 52.12 |
| Francisco Vega | 2 | 0 | 0.34 |