FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2548345880> ?p ?o ?g. }

• W2548345880 endingPage "128" @default.
• W2548345880 startingPage "116" @default.
• W2548345880 abstract "Managing intermittent demand is a vital task in several industrial contexts, and good forecasting ability is a fundamental prerequisite for an efficient inventory control system in stochastic environments. In recent years, research has been conducted on single-hidden layer feedforward neural networks, with promising results. In particular, back-propagation has been adopted as a gradient descent-based algorithm for training networks. However, when managing a large number of items, it is not feasible to optimize networks at item level, due to the effort required for tuning the parameters during the training stage. A simpler and faster learning algorithm, called the extreme learning machine, has been therefore proposed in the literature to address this issue, but it has never been tried for forecasting intermittent demand. On the one hand, an extensive comparison of single-hidden layer networks trained by back-propagation is required to improve our understanding of them as predictors of intermittent demand. On the other hand, it is also worth testing extreme learning machines in this context, because of their lower computational complexity and good generalisation ability. In this paper, neural networks trained by back-propagation and extreme learning machines are compared with benchmark neural networks, as well as standard forecasting methods for intermittent demand on real-time series, by combining different input patterns and architectures. A statistical analysis is then conducted to validate the best performance through different aggregation levels. Finally, some insights for practitioners are presented to improve the potential of neural networks for implementation in real environments." @default.
• W2548345880 created "2016-11-11" @default.
• W2548345880 creator A5005470492 @default.
• W2548345880 creator A5024155850 @default.
• W2548345880 creator A5058109404 @default.
• W2548345880 creator A5074673282 @default.
• W2548345880 creator A5078125999 @default.
• W2548345880 creator A5086491338 @default.
• W2548345880 date "2017-01-01" @default.
• W2548345880 modified "2023-10-06" @default.
• W2548345880 title "Single-hidden layer neural networks for forecasting intermittent demand" @default.
• W2548345880 cites W1484796914 @default.
• W2548345880 cites W1498436455 @default.
• W2548345880 cites W1965821767 @default.
• W2548345880 cites W1967829705 @default.
• W2548345880 cites W1972835575 @default.
• W2548345880 cites W1974283773 @default.
• W2548345880 cites W1974511160 @default.
• W2548345880 cites W1975938969 @default.
• W2548345880 cites W1978784367 @default.
• W2548345880 cites W1980287119 @default.
• W2548345880 cites W1980462675 @default.
• W2548345880 cites W1982348007 @default.
• W2548345880 cites W1989787309 @default.
• W2548345880 cites W1997754540 @default.
• W2548345880 cites W2006650150 @default.
• W2548345880 cites W2011227258 @default.
• W2548345880 cites W2011417860 @default.
• W2548345880 cites W2015071911 @default.
• W2548345880 cites W2016043834 @default.
• W2548345880 cites W2031155980 @default.
• W2548345880 cites W2036600452 @default.
• W2548345880 cites W2037072271 @default.
• W2548345880 cites W2044556794 @default.
• W2548345880 cites W2046432185 @default.
• W2548345880 cites W2048665112 @default.
• W2548345880 cites W2050099778 @default.
• W2548345880 cites W2051053942 @default.
• W2548345880 cites W2054008322 @default.
• W2548345880 cites W2065213709 @default.
• W2548345880 cites W2071662141 @default.
• W2548345880 cites W2074121977 @default.
• W2548345880 cites W2088897322 @default.
• W2548345880 cites W2094100331 @default.
• W2548345880 cites W2095081606 @default.
• W2548345880 cites W2095220782 @default.
• W2548345880 cites W2099639456 @default.
• W2548345880 cites W2101906966 @default.
• W2548345880 cites W2109110668 @default.
• W2548345880 cites W2111072639 @default.
• W2548345880 cites W2114733835 @default.
• W2548345880 cites W2121971770 @default.
• W2548345880 cites W2136192534 @default.
• W2548345880 cites W2146552111 @default.
• W2548345880 cites W2149921893 @default.
• W2548345880 cites W2150414962 @default.
• W2548345880 cites W2154326182 @default.
• W2548345880 cites W2167982865 @default.
• W2548345880 cites W2169228003 @default.
• W2548345880 cites W2169976759 @default.
• W2548345880 cites W2209764198 @default.
• W2548345880 cites W2218112468 @default.
• W2548345880 doi "https://doi.org/10.1016/j.ijpe.2016.10.021" @default.
• W2548345880 hasPublicationYear "2017" @default.
• W2548345880 type Work @default.
• W2548345880 sameAs 2548345880 @default.
• W2548345880 citedByCount "81" @default.
• W2548345880 countsByYear W25483458802017 @default.
• W2548345880 countsByYear W25483458802018 @default.
• W2548345880 countsByYear W25483458802019 @default.
• W2548345880 countsByYear W25483458802020 @default.
• W2548345880 countsByYear W25483458802021 @default.
• W2548345880 countsByYear W25483458802022 @default.
• W2548345880 countsByYear W25483458802023 @default.
• W2548345880 crossrefType "journal-article" @default.
• W2548345880 hasAuthorship W2548345880A5005470492 @default.
• W2548345880 hasAuthorship W2548345880A5024155850 @default.
• W2548345880 hasAuthorship W2548345880A5058109404 @default.
• W2548345880 hasAuthorship W2548345880A5074673282 @default.
• W2548345880 hasAuthorship W2548345880A5078125999 @default.
• W2548345880 hasAuthorship W2548345880A5086491338 @default.
• W2548345880 hasBestOaLocation W25483458802 @default.
• W2548345880 hasConcept C119857082 @default.
• W2548345880 hasConcept C127413603 @default.
• W2548345880 hasConcept C13280743 @default.
• W2548345880 hasConcept C133731056 @default.
• W2548345880 hasConcept C151730666 @default.
• W2548345880 hasConcept C153258448 @default.
• W2548345880 hasConcept C154945302 @default.
• W2548345880 hasConcept C155032097 @default.
• W2548345880 hasConcept C185798385 @default.
• W2548345880 hasConcept C193809577 @default.
• W2548345880 hasConcept C205649164 @default.
• W2548345880 hasConcept C2779343474 @default.
• W2548345880 hasConcept C2780150128 @default.
• W2548345880 hasConcept C38858127 @default.
• W2548345880 hasConcept C41008148 @default.
• W2548345880 hasConcept C42475967 @default.

• next