Name
Abstract | ||
|---|---|---|
Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. In this paper, we use recurrent technique to overcome these problems and propose a new CMAC neural network, named recurrent fuzzy CMAC (RFCMAC). Since the structure of RFCMAC is more complex, normal training methods are difficult to be applied. A new simple algorithm with a time-varying learning rate is proposed to assure the learning algorithm is stable. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72383-7_58 | ISNN (1) |
Keywords | Field | DocType |
nonlinear system,neural network | Nonlinear system,Pattern recognition,Computer science,Fuzzy cmac,Cerebellar model articulation controller,Artificial intelligence,SIMPLE algorithm,Artificial neural network,Dynamic neural network,Machine learning | Conference |
Volume | ISSN | Citations |
4491 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Floriberto Ortiz Rodriguez | 1 | 62 | 5.87 |
| Wen Yu | 2 | 283 | 22.70 |
| Marco A. Moreno-Armendariz | 3 | 71 | 12.12 |
| Xiaoou Li | 4 | 550 | 61.95 |