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Autor(en): 
  • David J. Murray-Smith
  • Testing and Validation of Computer Simulation Models: Principles, Methods and Applications 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  August 2016  
    Genre:  EDV / Informatik 
     
    Algorithms & data structures / Applied mathematics / B / Computer mathematics / Computer Modelling / computer science / Computer science—Mathematics / Computer simulation / Control Structures and Microprogramming / Engineering mathematics / HUMAN PHYSIOLOGY / Mathematical and Computational Engineering / Mathematical and Computational Engineering Applications / Mathematical Applications in Computer Science / Mathematical modelling / Maths for engineers / Microprogramming / PHYSIOLOGY / Simulation and Modeling
    ISBN:  9783319362663 
    EAN-Code: 
    9783319362663 
    Verlag:  Springer Nature EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Simulation Foundations, Methods and Applications  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  4102 gr 
    Seiten:  252 
    Zus. Info:  Previously published in hardcover 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This must-read text/reference provides a practical guide to processes involved in the development and application of dynamic simulation models, covering a wide range of issues relating to testing, verification and validation. Illustrative example problems in continuous system simulation are presented throughout the book, supported by extended case studies from a number of interdisciplinary applications. Topics and features: provides an emphasis on practical issues of model quality and validation, along with questions concerning the management of simulation models, the use of model libraries, and generic models; contains numerous step-by-step examples; presents detailed case studies, often with accompanying datasets; includes discussion of hybrid models, which involve a combination of continuous system and discrete-event descriptions; examines experimental modeling approaches that involve system identification and parameter estimation; offers supplementary material at an associated website.
      



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