Matches in SemOpenAlex for { <https://semopenalex.org/work/W2124350323> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W2124350323 endingPage "1004" @default.
- W2124350323 startingPage "995" @default.
- W2124350323 abstract "This paper presents a comparative analysis of different connectionist and statistical models for forecasting the weather of Vancouver, Canada. For developing the models, one year's data comprising of daily temperature and wind speed were used. A multi-layered perceptron network (MLPN) and an Elman recurrent neural network (ERNN) were trained using the one-step-secant and Levenberg–Marquardt algorithm. Radial basis function network (RBFN) was employed as an alternative to examine its applicability for weather forecasting. To ensure the effectiveness of neurocomputing techniques, the connectionist models were trained and tested using different datasets. Moreover, ensembles of the neural networks were generated by combining the MLPN, ERNN and RBFN using arithmetic mean and weighted average methods. Subsequently, performance of the connectionist models and their ensembles were compared with a well-established statistical technique. Experimental results obtained have shown RBFN produced the most accurate forecast model compared to ERNN and MLPN. Overall, the proposed ensemble approach produced the most accurate forecast, while the statistical model was relatively less accurate for the weather forecasting problem considered." @default.
- W2124350323 created "2016-06-24" @default.
- W2124350323 creator A5048950384 @default.
- W2124350323 creator A5087542455 @default.
- W2124350323 date "2007-06-01" @default.
- W2124350323 modified "2023-09-30" @default.
- W2124350323 title "Weather analysis using ensemble of connectionist learning paradigms" @default.
- W2124350323 cites W1990166396 @default.
- W2124350323 cites W1992880712 @default.
- W2124350323 cites W2001675020 @default.
- W2124350323 cites W2022290342 @default.
- W2124350323 cites W2031296078 @default.
- W2124350323 cites W2050220083 @default.
- W2124350323 cites W2069715036 @default.
- W2124350323 cites W2080690725 @default.
- W2124350323 cites W2100805904 @default.
- W2124350323 cites W2105779206 @default.
- W2124350323 cites W2110485445 @default.
- W2124350323 cites W2113442785 @default.
- W2124350323 cites W2123207935 @default.
- W2124350323 cites W2131408671 @default.
- W2124350323 cites W2134383396 @default.
- W2124350323 cites W2135293965 @default.
- W2124350323 cites W2136591887 @default.
- W2124350323 cites W2160203977 @default.
- W2124350323 cites W2160598920 @default.
- W2124350323 cites W2164353564 @default.
- W2124350323 cites W2164568552 @default.
- W2124350323 cites W2165199504 @default.
- W2124350323 cites W2165368814 @default.
- W2124350323 cites W2799536519 @default.
- W2124350323 cites W28412257 @default.
- W2124350323 cites W4212883601 @default.
- W2124350323 cites W4239414618 @default.
- W2124350323 cites W4242141328 @default.
- W2124350323 doi "https://doi.org/10.1016/j.asoc.2006.06.005" @default.
- W2124350323 hasPublicationYear "2007" @default.
- W2124350323 type Work @default.
- W2124350323 sameAs 2124350323 @default.
- W2124350323 citedByCount "35" @default.
- W2124350323 countsByYear W21243503232012 @default.
- W2124350323 countsByYear W21243503232013 @default.
- W2124350323 countsByYear W21243503232014 @default.
- W2124350323 countsByYear W21243503232015 @default.
- W2124350323 countsByYear W21243503232016 @default.
- W2124350323 countsByYear W21243503232018 @default.
- W2124350323 countsByYear W21243503232020 @default.
- W2124350323 countsByYear W21243503232021 @default.
- W2124350323 countsByYear W21243503232022 @default.
- W2124350323 countsByYear W21243503232023 @default.
- W2124350323 crossrefType "journal-article" @default.
- W2124350323 hasAuthorship W2124350323A5048950384 @default.
- W2124350323 hasAuthorship W2124350323A5087542455 @default.
- W2124350323 hasConcept C119857082 @default.
- W2124350323 hasConcept C132917294 @default.
- W2124350323 hasConcept C154945302 @default.
- W2124350323 hasConcept C179717631 @default.
- W2124350323 hasConcept C41008148 @default.
- W2124350323 hasConcept C50644808 @default.
- W2124350323 hasConcept C60908668 @default.
- W2124350323 hasConcept C8521452 @default.
- W2124350323 hasConcept C98856871 @default.
- W2124350323 hasConceptScore W2124350323C119857082 @default.
- W2124350323 hasConceptScore W2124350323C132917294 @default.
- W2124350323 hasConceptScore W2124350323C154945302 @default.
- W2124350323 hasConceptScore W2124350323C179717631 @default.
- W2124350323 hasConceptScore W2124350323C41008148 @default.
- W2124350323 hasConceptScore W2124350323C50644808 @default.
- W2124350323 hasConceptScore W2124350323C60908668 @default.
- W2124350323 hasConceptScore W2124350323C8521452 @default.
- W2124350323 hasConceptScore W2124350323C98856871 @default.
- W2124350323 hasIssue "3" @default.
- W2124350323 hasLocation W21243503231 @default.
- W2124350323 hasOpenAccess W2124350323 @default.
- W2124350323 hasPrimaryLocation W21243503231 @default.
- W2124350323 hasRelatedWork W1598419248 @default.
- W2124350323 hasRelatedWork W1997128020 @default.
- W2124350323 hasRelatedWork W2096715552 @default.
- W2124350323 hasRelatedWork W2298381223 @default.
- W2124350323 hasRelatedWork W2625424124 @default.
- W2124350323 hasRelatedWork W2941320171 @default.
- W2124350323 hasRelatedWork W3133467804 @default.
- W2124350323 hasRelatedWork W4231994957 @default.
- W2124350323 hasRelatedWork W960661091 @default.
- W2124350323 hasRelatedWork W2276110787 @default.
- W2124350323 hasVolume "7" @default.
- W2124350323 isParatext "false" @default.
- W2124350323 isRetracted "false" @default.
- W2124350323 magId "2124350323" @default.
- W2124350323 workType "article" @default.