Last edited by Tor
Friday, May 1, 2020 | History

10 edition of Metamodeling for method engineering found in the catalog.

Metamodeling for method engineering

Metamodeling for method engineering

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  • 27 Currently reading

Published by MIT Press in Cambridge, Ma .
Written in

    Subjects:
  • Programming (Mathematics),
  • Engineering models

  • Edition Notes

    Includes bibliographical references and index.

    Statementedited by Manfred A. Jeusfeld, Matthias Jarke, John Mylopoulos.
    SeriesCooperative information systems
    ContributionsJeusfeld, Manfred., Jarke, Matthias., Mylopoulos, John.
    Classifications
    LC ClassificationsT57.7 .M48 2005
    The Physical Object
    Paginationp. cm.
    ID Numbers
    Open LibraryOL3310844M
    ISBN 100262101084
    LC Control Number2004061363

    Endorsement of the book 'Metamodeling for Method Engineering' Method engineering has emerged in response to the need to adapt methods to better fit the requirements of the development task at hand. Its aim is to provide techniques for modelling reusable method components, adapting and assembling these together to form the new method. Author: Colette Rolland.   This research focuses on the study of the relationships between sample data characteristics and metamodel performance considering different types of metamodeling methods. In this work, four types of metamodeling methods, including multivariate polynomial method, radial basis function method, kriging method and Bayesian neural network method, three sample quality merits, Cited by:

    10/01/ "I'd like to suggest you read Patrice Micouin's "Model-Based Systems Engineering - Fundamentals and Methods". Perhaps after you've read Patrice's work on his Property-Model Methodology (PMM) and its concept of Property-Based Requirements (PBR) you might begin think differently and then again maybe not.".Cited by: (English) In: Metamodeling for Method Engineering / [ed] Manfred Jeusfeld, Matthias Jarke, John Mylopoulos, Cambridge, Massachusetts, USA: MIT Press, Chapter in book (Other academic) Abstract [en] This chapter provides a practical guide on how to use the meta datarepository ConceptBase to design information modeling methods by using meta-modeling. Relaxing the assumption on the differentiability of the simulation output makes the MLMC method more widely applicable to stochastic simulation metamodeling problems in industrial engineering. The proposed scheme uses a sequential experiment design which allocates effort unevenly among design points in order to increase its by: 2.

    D [Software Engineering]: Design|Methodologies, Representation General Terms Metamodel, Design Process, Domain Speci c Language Keywords Metamodel, Metamodeling Rules, Domain Speci c Model-ing, Design Process, Situational Method Engineering, Stan-dard 1. INTRODUCTION AND MOTIVATION Traditionally, in software engineering, the idea, or the con-. Model integrated computing (MIC) is an effective and efficient method for developing, maintaining, and evolving large-scale, domain-specific software applications for computer-based systems (CBSs). On a higher level, it is possible to use MIC to develop, maintain, and evolve the meta-level tools (metamodeling environments) themselves, by Cited by:   Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation by:


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Metamodeling for method engineering Download PDF EPUB FB2

SyntaxTextGen not activatedThe book first presents the theoretical pdf of metamodeling for method engineering, discussing information modeling, the potential of metamodeling for software systems development, and the.A metamodeling optimization system for nonlinear problems was developed in this study.

Boundaries and best neighbors searching (BBNS) intelligent sampling method and fuzzy based progressive metamodeling for space reduction were integrated and applied for this by: Ebook material flow behavior is an essential step to design and optimize the forming process.

Ebook this context, four popular metamodel types Kriging, radial basis function, multivariate polynomial, and artificial neural network are investigated as potential methods for modeling the flow behavior of aluminum alloy.

Based on the experimental data from hot compression tests, the modeling Author: Gang Xiao, Qinwen Yang, Luoxing Li, Zhengbing Xu.