Matches in SemOpenAlex for { <https://semopenalex.org/work/W3098889431> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W3098889431 endingPage "206733" @default.
- W3098889431 startingPage "206719" @default.
- W3098889431 abstract "In the intelligent ship field, with the upgrading of ship maintenance mode, the human-centered system maintenance will be gradually replaced by the artificial intelligence decision methods. To improve the training speed and testing accuracy of the state estimation model, an optimized Support Vector Machine (SVM) driven approach by Improved Artificial Bee Colony (IABC) was proposed to solve the global parameters optimization problem. First, the IABC method was achieved from three aspects: nectar source initializing, employed bee global neighborhood searching, and scouts mutation neighborhood searching. Second, the multi-class SVM with one-against-one classifiers was selected, and the best global parameters were achieved by the IABC. Third, the optimized SVM model was adopted in the testing to verify the effectiveness of state estimation. Finally, the elaborated methodology was applied to two actual ship systems to get the analysis results. The effectiveness was verified by using two examples. The results show the following: the IABC optimized SVM can obtain the global optimal parameters at a faster speed than the traditional ABC optimized method; the IABC optimized method can help the training start with better initial parameters, and get a higher classification accuracy rate than the traditional ABC optimized method. Based on the comparative analysis results, the IABC optimized SVM shows an obvious advantage of parameter optimization in the training process, and it can also significantly improve the model training efficiency and achieve a higher state estimation accuracy. The optimized SVM by IABC is an effective state estimation method in ship systems." @default.
- W3098889431 created "2020-11-23" @default.
- W3098889431 creator A5000981879 @default.
- W3098889431 creator A5023124128 @default.
- W3098889431 creator A5035775512 @default.
- W3098889431 creator A5068176076 @default.
- W3098889431 creator A5078165613 @default.
- W3098889431 date "2020-01-01" @default.
- W3098889431 modified "2023-09-24" @default.
- W3098889431 title "Optimized SVM-Driven Multi-Class Approach by Improved ABC to Estimating Ship Systems State" @default.
- W3098889431 cites W17188798 @default.
- W3098889431 cites W1984901605 @default.
- W3098889431 cites W1997379681 @default.
- W3098889431 cites W2055270694 @default.
- W3098889431 cites W2087347434 @default.
- W3098889431 cites W2101819884 @default.
- W3098889431 cites W2107074288 @default.
- W3098889431 cites W2172000360 @default.
- W3098889431 cites W2275872060 @default.
- W3098889431 cites W2319278070 @default.
- W3098889431 cites W2546863301 @default.
- W3098889431 cites W2766830746 @default.
- W3098889431 cites W2776185886 @default.
- W3098889431 cites W2793032506 @default.
- W3098889431 cites W2799753289 @default.
- W3098889431 cites W2884105239 @default.
- W3098889431 cites W2896990617 @default.
- W3098889431 cites W2905406381 @default.
- W3098889431 cites W2930439125 @default.
- W3098889431 cites W2968993717 @default.
- W3098889431 cites W2990512282 @default.
- W3098889431 cites W3000434541 @default.
- W3098889431 cites W3016995727 @default.
- W3098889431 cites W3035239908 @default.
- W3098889431 cites W3044999224 @default.
- W3098889431 cites W4239510810 @default.
- W3098889431 cites W4240975930 @default.
- W3098889431 doi "https://doi.org/10.1109/access.2020.3037251" @default.
- W3098889431 hasPublicationYear "2020" @default.
- W3098889431 type Work @default.
- W3098889431 sameAs 3098889431 @default.
- W3098889431 citedByCount "3" @default.
- W3098889431 countsByYear W30988894312021 @default.
- W3098889431 countsByYear W30988894312022 @default.
- W3098889431 countsByYear W30988894312023 @default.
- W3098889431 crossrefType "journal-article" @default.
- W3098889431 hasAuthorship W3098889431A5000981879 @default.
- W3098889431 hasAuthorship W3098889431A5023124128 @default.
- W3098889431 hasAuthorship W3098889431A5035775512 @default.
- W3098889431 hasAuthorship W3098889431A5068176076 @default.
- W3098889431 hasAuthorship W3098889431A5078165613 @default.
- W3098889431 hasBestOaLocation W30988894311 @default.
- W3098889431 hasConcept C111919701 @default.
- W3098889431 hasConcept C114466953 @default.
- W3098889431 hasConcept C119857082 @default.
- W3098889431 hasConcept C12267149 @default.
- W3098889431 hasConcept C154945302 @default.
- W3098889431 hasConcept C199360897 @default.
- W3098889431 hasConcept C41008148 @default.
- W3098889431 hasConcept C98045186 @default.
- W3098889431 hasConceptScore W3098889431C111919701 @default.
- W3098889431 hasConceptScore W3098889431C114466953 @default.
- W3098889431 hasConceptScore W3098889431C119857082 @default.
- W3098889431 hasConceptScore W3098889431C12267149 @default.
- W3098889431 hasConceptScore W3098889431C154945302 @default.
- W3098889431 hasConceptScore W3098889431C199360897 @default.
- W3098889431 hasConceptScore W3098889431C41008148 @default.
- W3098889431 hasConceptScore W3098889431C98045186 @default.
- W3098889431 hasFunder F4320321001 @default.
- W3098889431 hasLocation W30988894311 @default.
- W3098889431 hasOpenAccess W3098889431 @default.
- W3098889431 hasPrimaryLocation W30988894311 @default.
- W3098889431 hasRelatedWork W1996541855 @default.
- W3098889431 hasRelatedWork W2355927362 @default.
- W3098889431 hasRelatedWork W2368370270 @default.
- W3098889431 hasRelatedWork W2374442885 @default.
- W3098889431 hasRelatedWork W2374512474 @default.
- W3098889431 hasRelatedWork W2937631562 @default.
- W3098889431 hasRelatedWork W2961085424 @default.
- W3098889431 hasRelatedWork W3195168932 @default.
- W3098889431 hasRelatedWork W4306674287 @default.
- W3098889431 hasRelatedWork W4224009465 @default.
- W3098889431 hasVolume "8" @default.
- W3098889431 isParatext "false" @default.
- W3098889431 isRetracted "false" @default.
- W3098889431 magId "3098889431" @default.
- W3098889431 workType "article" @default.