Neural Network and Regression Spline Value Function Approximations for Stochastic Dynamic Programming (Articolo in rivista)

Type
Label
  • Neural Network and Regression Spline Value Function Approximations for Stochastic Dynamic Programming (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.cor.2005.02.043 (literal)
Alternative label
  • Cristiano Cervellera; Aihong Wen; Victoria C.P. Chen (2007)
    Neural Network and Regression Spline Value Function Approximations for Stochastic Dynamic Programming
    in Computers & operations research
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Cristiano Cervellera; Aihong Wen; Victoria C.P. Chen (literal)
Pagina inizio
  • 70 (literal)
Pagina fine
  • 90 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 34 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Cristiano Cervellera: Institute of Intelligent Systems for Automation-ISSIA-CNR, National Research Council of Italy, Via De Marini 6, 16149 Genova, Italy Aihong Wen, Victoria C.P. Chen: Department of Industrial & Manufacturing Systems Engineering, The University of Texas at Arlington, Campus Box 19017, Arlington, TX 76019-0017, USA (literal)
Titolo
  • Neural Network and Regression Spline Value Function Approximations for Stochastic Dynamic Programming (literal)
Abstract
  • Dynamic programming is a multi-stage optimization method that is applicable to many problems in engineering. A statistical perspective of value function approximation in high-dimensional, continuous-state stochastic dynamic programming (SDP) was first presented using orthogonal array (OA) experimental designs and multivariate adaptive regression splines (MARS). Given the popularity of artificial neural networks (ANNs) for high-dimensional modeling in engineering, this paper presents an implementation of ANNs as an alternative to MARS. Comparisons consider the differences in methodological objectives, computational complexity, model accuracy, and numerical SDP solutions. Two applications are presented: a nine-dimensional inventory forecasting problem and an eight-dimensional water reservoir problem. Both OAs and OA-based Latin hypercube experimental designs are explored, and OA space-filling quality is considered. (literal)
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