Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210348535> ?p ?o ?g. }
- W4210348535 endingPage "8" @default.
- W4210348535 startingPage "1" @default.
- W4210348535 abstract "Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragmentation X50 is selected as the output factor of the prediction model and compared with the results of PSO-ELM and ELM. The results show that MAPE of GWO-ELM, PSO-ELM, and ELM are 1.78%, 5.40%, and 10.90%, respectively; their RMSE are 0.007, 0.022, and 0.045, respectively. The ELM model optimized by the gray wolf optimizer is more accurate and has stronger data fitting ability than PSO-ELM and ELM models, and the prediction accuracy of GWO-ELM is much higher than that of PSO-ELM and ELM." @default.
- W4210348535 created "2022-02-08" @default.
- W4210348535 creator A5000567085 @default.
- W4210348535 creator A5056759575 @default.
- W4210348535 creator A5075039111 @default.
- W4210348535 creator A5083400702 @default.
- W4210348535 date "2022-01-30" @default.
- W4210348535 modified "2023-09-26" @default.
- W4210348535 title "Prediction of Blasting Fragmentation Based on GWO-ELM" @default.
- W4210348535 cites W1731892004 @default.
- W4210348535 cites W1973677706 @default.
- W4210348535 cites W1976685072 @default.
- W4210348535 cites W2023586206 @default.
- W4210348535 cites W2030243757 @default.
- W4210348535 cites W2075622495 @default.
- W4210348535 cites W2075918951 @default.
- W4210348535 cites W2101881769 @default.
- W4210348535 cites W2170756561 @default.
- W4210348535 cites W2194384066 @default.
- W4210348535 cites W2345064654 @default.
- W4210348535 cites W2427322446 @default.
- W4210348535 cites W2559066004 @default.
- W4210348535 cites W2566601976 @default.
- W4210348535 cites W2744687549 @default.
- W4210348535 cites W2930688108 @default.
- W4210348535 cites W2964938317 @default.
- W4210348535 cites W2999044379 @default.
- W4210348535 cites W3001544435 @default.
- W4210348535 cites W3011092893 @default.
- W4210348535 cites W3013461775 @default.
- W4210348535 cites W3041338387 @default.
- W4210348535 cites W3087475814 @default.
- W4210348535 cites W3090513373 @default.
- W4210348535 cites W3093358807 @default.
- W4210348535 cites W3097563289 @default.
- W4210348535 cites W3111926949 @default.
- W4210348535 cites W3120283229 @default.
- W4210348535 cites W3126813375 @default.
- W4210348535 cites W3131554761 @default.
- W4210348535 cites W3132558967 @default.
- W4210348535 cites W3138121153 @default.
- W4210348535 cites W3138306058 @default.
- W4210348535 cites W3141715560 @default.
- W4210348535 cites W3153954529 @default.
- W4210348535 cites W3155137073 @default.
- W4210348535 cites W3157471513 @default.
- W4210348535 cites W3159681033 @default.
- W4210348535 cites W3159866605 @default.
- W4210348535 cites W3164621419 @default.
- W4210348535 cites W3175821757 @default.
- W4210348535 doi "https://doi.org/10.1155/2022/7385456" @default.
- W4210348535 hasPublicationYear "2022" @default.
- W4210348535 type Work @default.
- W4210348535 citedByCount "3" @default.
- W4210348535 countsByYear W42103485352022 @default.
- W4210348535 countsByYear W42103485352023 @default.
- W4210348535 crossrefType "journal-article" @default.
- W4210348535 hasAuthorship W4210348535A5000567085 @default.
- W4210348535 hasAuthorship W4210348535A5056759575 @default.
- W4210348535 hasAuthorship W4210348535A5075039111 @default.
- W4210348535 hasAuthorship W4210348535A5083400702 @default.
- W4210348535 hasBestOaLocation W42103485351 @default.
- W4210348535 hasConcept C111919701 @default.
- W4210348535 hasConcept C11413529 @default.
- W4210348535 hasConcept C127413603 @default.
- W4210348535 hasConcept C154945302 @default.
- W4210348535 hasConcept C187320778 @default.
- W4210348535 hasConcept C191015642 @default.
- W4210348535 hasConcept C2780150128 @default.
- W4210348535 hasConcept C33923547 @default.
- W4210348535 hasConcept C41008148 @default.
- W4210348535 hasConcept C50644808 @default.
- W4210348535 hasConcept C50933969 @default.
- W4210348535 hasConcept C66938386 @default.
- W4210348535 hasConceptScore W4210348535C111919701 @default.
- W4210348535 hasConceptScore W4210348535C11413529 @default.
- W4210348535 hasConceptScore W4210348535C127413603 @default.
- W4210348535 hasConceptScore W4210348535C154945302 @default.
- W4210348535 hasConceptScore W4210348535C187320778 @default.
- W4210348535 hasConceptScore W4210348535C191015642 @default.
- W4210348535 hasConceptScore W4210348535C2780150128 @default.
- W4210348535 hasConceptScore W4210348535C33923547 @default.
- W4210348535 hasConceptScore W4210348535C41008148 @default.
- W4210348535 hasConceptScore W4210348535C50644808 @default.
- W4210348535 hasConceptScore W4210348535C50933969 @default.
- W4210348535 hasConceptScore W4210348535C66938386 @default.
- W4210348535 hasFunder F4320321001 @default.
- W4210348535 hasLocation W42103485351 @default.
- W4210348535 hasLocation W42103485352 @default.
- W4210348535 hasOpenAccess W4210348535 @default.
- W4210348535 hasPrimaryLocation W42103485351 @default.
- W4210348535 hasRelatedWork W1968085247 @default.
- W4210348535 hasRelatedWork W2360138065 @default.
- W4210348535 hasRelatedWork W2362868842 @default.
- W4210348535 hasRelatedWork W2374545240 @default.
- W4210348535 hasRelatedWork W2390636758 @default.
- W4210348535 hasRelatedWork W2429215549 @default.
- W4210348535 hasRelatedWork W2521402969 @default.