Matches in SemOpenAlex for { <https://semopenalex.org/work/W3017730993> ?p ?o ?g. }
- W3017730993 endingPage "2926" @default.
- W3017730993 startingPage "2926" @default.
- W3017730993 abstract "In order to accurately predict the erosion effect of underwater cleaning with an angle nozzle under different working conditions, this paper uses refractory bricks to simulate marine fouling as the erosion target, and studies the optimized erosion prediction model by erosion test based on the submerged low-pressure water jet. The erosion test is conducted by orthogonal experimental design, and experimental data are used for the prediction model. By combining with statistical range and variance analysis methods, the jet pressure, impact time and jet angle are determined as three inputs of the prediction model, and erosion depth is the output index of the prediction model. A virtual data generation method is used to increase the amount of input data for the prediction model. This paper also proposes a Mind-evolved Advanced Genetic Algorithm (MAGA), which has a reliable optimization effect in the verification of four stand test functions. Then, the improved back-propagating (BP) neural network prediction models are established by respectively using Genetic Algorithm (GA) and MAGA optimization algorithms to optimize the initial thresholds and weights of the BP neural network. Compared to the prediction results of the BP and GA-BP models, the R2 of the MAGA-BP model is the highest, reaching 0.9954; the total error is reduced by 47.31% and 35.01%; the root mean square error decreases by 51.05% and 31.80%; and the maximum absolute percentage error decreases by 65.79% and 64.01%, respectively. The average prediction accuracy of the MAGA-BP model is controlled within 3%, which has been significantly improved. The results show that the prediction accuracy of the MAGA-BP prediction model is higher and more reliable, and the MAGA algorithm has a good optimization effect. This optimized erosion prediction method is feasible." @default.
- W3017730993 created "2020-05-01" @default.
- W3017730993 creator A5011490550 @default.
- W3017730993 creator A5029577636 @default.
- W3017730993 creator A5032633665 @default.
- W3017730993 creator A5044291873 @default.
- W3017730993 creator A5044974449 @default.
- W3017730993 date "2020-04-23" @default.
- W3017730993 modified "2023-10-17" @default.
- W3017730993 title "Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet" @default.
- W3017730993 cites W1498436455 @default.
- W3017730993 cites W2010085354 @default.
- W3017730993 cites W2020485833 @default.
- W3017730993 cites W2037299980 @default.
- W3017730993 cites W2064855837 @default.
- W3017730993 cites W2092195737 @default.
- W3017730993 cites W2144792636 @default.
- W3017730993 cites W2294449738 @default.
- W3017730993 cites W2406186729 @default.
- W3017730993 cites W2568870724 @default.
- W3017730993 cites W2591451067 @default.
- W3017730993 cites W2735772032 @default.
- W3017730993 cites W2769055182 @default.
- W3017730993 cites W2908833417 @default.
- W3017730993 cites W2944450163 @default.
- W3017730993 cites W2945694963 @default.
- W3017730993 cites W2961264342 @default.
- W3017730993 cites W2973117460 @default.
- W3017730993 cites W2994856725 @default.
- W3017730993 cites W4235672053 @default.
- W3017730993 doi "https://doi.org/10.3390/app10082926" @default.
- W3017730993 hasPublicationYear "2020" @default.
- W3017730993 type Work @default.
- W3017730993 sameAs 3017730993 @default.
- W3017730993 citedByCount "9" @default.
- W3017730993 countsByYear W30177309932020 @default.
- W3017730993 countsByYear W30177309932021 @default.
- W3017730993 countsByYear W30177309932022 @default.
- W3017730993 countsByYear W30177309932023 @default.
- W3017730993 crossrefType "journal-article" @default.
- W3017730993 hasAuthorship W3017730993A5011490550 @default.
- W3017730993 hasAuthorship W3017730993A5029577636 @default.
- W3017730993 hasAuthorship W3017730993A5032633665 @default.
- W3017730993 hasAuthorship W3017730993A5044291873 @default.
- W3017730993 hasAuthorship W3017730993A5044974449 @default.
- W3017730993 hasBestOaLocation W30177309931 @default.
- W3017730993 hasConcept C105795698 @default.
- W3017730993 hasConcept C11413529 @default.
- W3017730993 hasConcept C119857082 @default.
- W3017730993 hasConcept C119947313 @default.
- W3017730993 hasConcept C122383733 @default.
- W3017730993 hasConcept C123157820 @default.
- W3017730993 hasConcept C127313418 @default.
- W3017730993 hasConcept C127413603 @default.
- W3017730993 hasConcept C139945424 @default.
- W3017730993 hasConcept C146978453 @default.
- W3017730993 hasConcept C151730666 @default.
- W3017730993 hasConcept C154945302 @default.
- W3017730993 hasConcept C199104240 @default.
- W3017730993 hasConcept C204323151 @default.
- W3017730993 hasConcept C33923547 @default.
- W3017730993 hasConcept C41008148 @default.
- W3017730993 hasConcept C50644808 @default.
- W3017730993 hasConcept C56200935 @default.
- W3017730993 hasConcept C78519656 @default.
- W3017730993 hasConcept C8880873 @default.
- W3017730993 hasConceptScore W3017730993C105795698 @default.
- W3017730993 hasConceptScore W3017730993C11413529 @default.
- W3017730993 hasConceptScore W3017730993C119857082 @default.
- W3017730993 hasConceptScore W3017730993C119947313 @default.
- W3017730993 hasConceptScore W3017730993C122383733 @default.
- W3017730993 hasConceptScore W3017730993C123157820 @default.
- W3017730993 hasConceptScore W3017730993C127313418 @default.
- W3017730993 hasConceptScore W3017730993C127413603 @default.
- W3017730993 hasConceptScore W3017730993C139945424 @default.
- W3017730993 hasConceptScore W3017730993C146978453 @default.
- W3017730993 hasConceptScore W3017730993C151730666 @default.
- W3017730993 hasConceptScore W3017730993C154945302 @default.
- W3017730993 hasConceptScore W3017730993C199104240 @default.
- W3017730993 hasConceptScore W3017730993C204323151 @default.
- W3017730993 hasConceptScore W3017730993C33923547 @default.
- W3017730993 hasConceptScore W3017730993C41008148 @default.
- W3017730993 hasConceptScore W3017730993C50644808 @default.
- W3017730993 hasConceptScore W3017730993C56200935 @default.
- W3017730993 hasConceptScore W3017730993C78519656 @default.
- W3017730993 hasConceptScore W3017730993C8880873 @default.
- W3017730993 hasIssue "8" @default.
- W3017730993 hasLocation W30177309931 @default.
- W3017730993 hasLocation W30177309932 @default.
- W3017730993 hasLocation W30177309933 @default.
- W3017730993 hasOpenAccess W3017730993 @default.
- W3017730993 hasPrimaryLocation W30177309931 @default.
- W3017730993 hasRelatedWork W1974945169 @default.
- W3017730993 hasRelatedWork W2335067871 @default.
- W3017730993 hasRelatedWork W2359549665 @default.
- W3017730993 hasRelatedWork W2360768078 @default.
- W3017730993 hasRelatedWork W2363437875 @default.
- W3017730993 hasRelatedWork W2382761789 @default.