完整後設資料紀錄
DC 欄位語言
dc.contributor.authorChang, Jyh-Yeongen_US
dc.contributor.authorLiao, Shih-Huien_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2014-12-08T15:33:16Z-
dc.date.available2014-12-08T15:33:16Z-
dc.date.issued2012en_US
dc.identifier.isbn978-1-4673-2056-6en_US
dc.identifier.urihttp://hdl.handle.net/11536/23148-
dc.description.abstractIn this paper, we propose the adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies the scale estimate to the least trimmed squares fuzzy neural network (LTS-FNN). The emphasis of this paper is particular on the robustness against the outliers and the choice of the trimming constant can be determined adaptively. Some numerical examples will be provided to compare the robustness against outliers for usual FNN and the ALTS-FNN. Simulation results show that the ALTS-FNN in the paper have good performance for outlier detection.en_US
dc.language.isoen_USen_US
dc.subjectleast trimmed squares (LTS) estimatoren_US
dc.subjectfuzzy neural network (FNN)en_US
dc.subjectleast trimmed squares fuzzy neural network (LTS-FNN)en_US
dc.subjectadaptive least trimmed squares fuzzy neural network (ALTS-FNN)en_US
dc.titleAdaptive Least Trimmed Squares Fuzzy Neural Networken_US
dc.typeProceedings Paperen_US
dc.identifier.journal2012 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY2012)en_US
dc.citation.spage413en_US
dc.citation.epage416en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000326810700074-
顯示於類別:會議論文