Matches in SemOpenAlex for { <https://semopenalex.org/work/W2093729752> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W2093729752 endingPage "1068" @default.
- W2093729752 startingPage "1044" @default.
- W2093729752 abstract "Two new on-line algorithms for adaptive principal component analysis (APCA) are proposed and discussed in order to solve the problem of on-line industrial process monitoring in this paper. Both the algorithms have the capability of extracting principal component eigenvectors on-line in a fixed size sliding data window with high dimensional input data. The first algorithm is based on the steepest gradient descent approach, which updates the covariance matrix with deflation transformation and on-line iteration. Based on neural networks, the second algorithm constructs the input data sequence with an on-line iteration method and trains the neural network in every data frame. The convergence of the two algorithms is then analyzed and the simulations are given to illustrate the effectiveness of the two algorithms. At last, the applications of the two algorithms are discussed." @default.
- W2093729752 created "2016-06-24" @default.
- W2093729752 creator A5017993330 @default.
- W2093729752 creator A5039139894 @default.
- W2093729752 creator A5039318240 @default.
- W2093729752 creator A5052594325 @default.
- W2093729752 date "2012-04-01" @default.
- W2093729752 modified "2023-09-22" @default.
- W2093729752 title "On-line adaptive principal component extraction algorithms using iteration approach" @default.
- W2093729752 cites W2003516238 @default.
- W2093729752 cites W2040703746 @default.
- W2093729752 cites W2110121428 @default.
- W2093729752 cites W2131347209 @default.
- W2093729752 cites W2140984045 @default.
- W2093729752 cites W2142231140 @default.
- W2093729752 cites W2143984002 @default.
- W2093729752 cites W2144436897 @default.
- W2093729752 cites W2149424795 @default.
- W2093729752 cites W2151859462 @default.
- W2093729752 cites W2155311268 @default.
- W2093729752 cites W2156338897 @default.
- W2093729752 cites W2161104322 @default.
- W2093729752 cites W2161486866 @default.
- W2093729752 doi "https://doi.org/10.1016/j.sigpro.2011.10.016" @default.
- W2093729752 hasPublicationYear "2012" @default.
- W2093729752 type Work @default.
- W2093729752 sameAs 2093729752 @default.
- W2093729752 citedByCount "7" @default.
- W2093729752 countsByYear W20937297522013 @default.
- W2093729752 countsByYear W20937297522014 @default.
- W2093729752 countsByYear W20937297522015 @default.
- W2093729752 countsByYear W20937297522016 @default.
- W2093729752 countsByYear W20937297522020 @default.
- W2093729752 crossrefType "journal-article" @default.
- W2093729752 hasAuthorship W2093729752A5017993330 @default.
- W2093729752 hasAuthorship W2093729752A5039139894 @default.
- W2093729752 hasAuthorship W2093729752A5039318240 @default.
- W2093729752 hasAuthorship W2093729752A5052594325 @default.
- W2093729752 hasConcept C11413529 @default.
- W2093729752 hasConcept C121332964 @default.
- W2093729752 hasConcept C126255220 @default.
- W2093729752 hasConcept C154945302 @default.
- W2093729752 hasConcept C168167062 @default.
- W2093729752 hasConcept C198352243 @default.
- W2093729752 hasConcept C2524010 @default.
- W2093729752 hasConcept C27438332 @default.
- W2093729752 hasConcept C33923547 @default.
- W2093729752 hasConcept C41008148 @default.
- W2093729752 hasConcept C97355855 @default.
- W2093729752 hasConceptScore W2093729752C11413529 @default.
- W2093729752 hasConceptScore W2093729752C121332964 @default.
- W2093729752 hasConceptScore W2093729752C126255220 @default.
- W2093729752 hasConceptScore W2093729752C154945302 @default.
- W2093729752 hasConceptScore W2093729752C168167062 @default.
- W2093729752 hasConceptScore W2093729752C198352243 @default.
- W2093729752 hasConceptScore W2093729752C2524010 @default.
- W2093729752 hasConceptScore W2093729752C27438332 @default.
- W2093729752 hasConceptScore W2093729752C33923547 @default.
- W2093729752 hasConceptScore W2093729752C41008148 @default.
- W2093729752 hasConceptScore W2093729752C97355855 @default.
- W2093729752 hasFunder F4320321001 @default.
- W2093729752 hasFunder F4320321106 @default.
- W2093729752 hasFunder F4320335787 @default.
- W2093729752 hasIssue "4" @default.
- W2093729752 hasLocation W20937297521 @default.
- W2093729752 hasOpenAccess W2093729752 @default.
- W2093729752 hasPrimaryLocation W20937297521 @default.
- W2093729752 hasRelatedWork W2001372204 @default.
- W2093729752 hasRelatedWork W2084166352 @default.
- W2093729752 hasRelatedWork W2349883060 @default.
- W2093729752 hasRelatedWork W2360796461 @default.
- W2093729752 hasRelatedWork W2361957794 @default.
- W2093729752 hasRelatedWork W2364925730 @default.
- W2093729752 hasRelatedWork W2379533788 @default.
- W2093729752 hasRelatedWork W2385928515 @default.
- W2093729752 hasRelatedWork W2778450995 @default.
- W2093729752 hasRelatedWork W2900763005 @default.
- W2093729752 hasVolume "92" @default.
- W2093729752 isParatext "false" @default.
- W2093729752 isRetracted "false" @default.
- W2093729752 magId "2093729752" @default.
- W2093729752 workType "article" @default.