NEURO-FUZZY MODELING AND CONTROL
| dc.citation.epage | 406 | en_US |
| dc.citation.issue | 3 | en_US |
| dc.citation.spage | 378 | en_US |
| dc.citation.volume | 83 | en_US |
| dc.citation.woscount | 783 | |
| dc.contributor.author | JANG, JSR | en_US |
| dc.contributor.author | SUN, CT | en_US |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| dc.contributor.department | Department of Computer Science | en_US |
| dc.date.accessioned | 2014-12-08T15:03:29Z | |
| dc.date.available | 2014-12-08T15:03:29Z | |
| dc.date.issued | 1995-03-01 | en_US |
| dc.description.abstract | Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called Adaptive-Network-based Fuzzy Inference System (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. | en_US |
| dc.identifier.doi | 10.1109/5.364486 | en_US |
| dc.identifier.issn | 0018-9219 | en_US |
| dc.identifier.journal | PROCEEDINGS OF THE IEEE | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1109/5.364486 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/2013 | |
| dc.identifier.wosnumber | WOS:A1995QJ99800003 | |
| dc.language.iso | en_US | en_US |
| dc.subject | FUZZY LOGIC | en_US |
| dc.subject | NEURAL NETWORKS | en_US |
| dc.subject | FUZZY MODELING | en_US |
| dc.subject | NEURO-FUZZY MODELING | en_US |
| dc.subject | NEURO-FUZZY CONTROL | en_US |
| dc.subject | ANFIS | en_US |
| dc.title | NEURO-FUZZY MODELING AND CONTROL | en_US |
| dc.type | Review | en_US |