Self-organizing fuzzy learning CLOS guidance law design
| dc.citation.epage | 1151 | en_US |
| dc.citation.issue | 4 | en_US |
| dc.citation.spage | 1144 | en_US |
| dc.citation.volume | 39 | en_US |
| dc.citation.woscount | 7 | |
| dc.contributor.author | Lin, CM | en_US |
| dc.contributor.author | Hsu, CF | en_US |
| dc.contributor.author | Mon, YJ | en_US |
| dc.contributor.department | 電控工程研究所 | zh_TW |
| dc.contributor.department | Institute of Electrical and Control Engineering | en_US |
| dc.date.accessioned | 2014-12-08T15:40:16Z | |
| dc.date.available | 2014-12-08T15:40:16Z | |
| dc.date.issued | 2003-10-01 | en_US |
| dc.description.abstract | A new self-organizing fuzzy logic control (SOFLC) design method is proposed. The proposed method is applied to the command line-of-sight (CLOS) guidance law design. The SOFLC contains two sets of fuzzy inference logic. One is the fuzzy logic controller and the other is the rule modifier. The new learning method of the rule modifier is developed based on a fuzzy learning algorithm. The modification value of each rule is based on the fuzzy firing weight, so that learning of the rule bases is reasonable. Finally, two engagement scenarios are examined, and a comparison between a fuzzy logic control (FLC), an optimal learning FLC, and the proposed SOFLC CLOS guidance laws is made. Simulation results show that the proposed SOFLC guidance law can achieve better guidance performance than the other guidance laws. | en_US |
| dc.identifier.doi | 10.1109/TAES.2003.1261118 | en_US |
| dc.identifier.issn | 0018-9251 | en_US |
| dc.identifier.journal | IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1109/TAES.2003.1261118 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/27488 | |
| dc.identifier.wosnumber | WOS:000188511200003 | |
| dc.language.iso | en_US | en_US |
| dc.title | Self-organizing fuzzy learning CLOS guidance law design | en_US |
| dc.type | Article | en_US |