Cover of: An introduction to the regenerative method for simulation analysis | M. A. Crane

An introduction to the regenerative method for simulation analysis

  • 111 Pages
  • 4.17 MB
  • 6719 Downloads
  • English
by
Springer-Verlag , Berlin, New York
Digital computer simulation., Stochastic processes., Estimation th
Other titlesRegenerative method for simulation analysis.
StatementM. A. Crane, A. J. Lemoine.
SeriesLecture notes in control and information sciences ;, 4
ContributionsLemoine, A. J., joint author.
Classifications
LC ClassificationsQA76.9.C65 C7
The Physical Object
Paginationvii, 111 p. ;
ID Numbers
Open LibraryOL4474462M
ISBN 100387084088
LC Control Number79301860

An Introduction to the Regenerative Method for Simulation Analysis (Lecture Notes in Control and Information Sciences (4)): Computer Science Books @ hor: M.A.

Crane, A.J. Lemoine. Discover An Introduction to the Regenerative Method for Simulation Analysis by M.A. Crane, A.J.

Lemoine and millions of other books available at Barnes & Noble. Shop paperbacks, eBooks, and more. Covid Safety Book Annex Membership Educators Gift Cards Stores & Events HelpPages: An Introduction to the Regenerative Method for Simulation Analysis. Authors: Crane, M.A., Lemoine, A.J. Free Preview.

An Introduction to the Regenerative Method for Simulation Analysis. k Downloads; Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 4) Chapters Table of contents (9 chapters) About About this book; Table of contents.

Search within book. Front Matter. PDF. Introduction. Pages Basic examples and. Introduction. Introduction. Point Estimators and Confidence Intervals. Examples of Regenerative Process. Selecting the Best Stable Stochastic System.

The Regenerative Method for constrained Optimization Problems. Variance Reduction Techniques. For example, Mykland et al. ()'s CLT requires E ν N 2 1 regenerative simulation is. () The regenerative method. In: Crane M.A., Lemoine A.J. (eds) An Introduction to the Regenerative Method for Simulation Analysis.

Lecture Notes. He is the author (or coauthor) of four books and numerous papers on simulation, manufacturing, operations research, and statistics. His article, "Statistical Analysis of Simulation Output Data," was the first invited feature paper on simulation to appear in a major research journal.

He won the An Introduction to the Regenerative Method for Simulation Analysis. Lecture Notes in Control and Information Sciences, Vol. Lecture Notes in Control and Information Sciences, Vol. An Introduction to the Regenerative Method for Simulation Analysis, Lecture Notes in Control and Information Sciences, Vol.

Details An introduction to the regenerative method for simulation analysis PDF

4, Spdnger-Verlag, New York. Ducket, S. and Pritsker, A. Examination of simulation output using spectral methods, Math. The regenerative method.- More examples of regenerative processes.- The regenerative approach and discrete-event simulations.- Approximation techniques.- Alternative ratio estimators.- Some other results.- Bibliographic note.

Series Title: Lecture notes in control and information sciences, 4. Other Titles: Regenerative method for simulation. Get this from a library.

Description An introduction to the regenerative method for simulation analysis FB2

An introduction to the regenerative method for simulation analysis. [Martin A Crane; Artur J Lemoine]. This paper reviews statistical methods for analyzing output data from computer simulations. Specifically, it focuses on the estimation of steady-state system parameters.

The estimation techniques. Payne, J. Introduction to simulation: Programming techniques and methods of analysis. An Introduction to the Regenerative Method for Simulation Analysis. Lecture Notes in Control and Information Sciences 4.

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Berlin-Heidelberg-New York, Springer-Verlag VII, S., 4 Abb., DM 18. US $ Stoyan, D. Abstract. Publication: Zeitschrift Angewandte Mathematik und Mechanik.

Pub Date: DOI: /zamm Theregenerative method(RM) of simulation output analysis [Crane and Iglehart ] uses this structure to construct asymptotically valid confidence intervals for the steady- state mean of a regenerative process. There are several settings in which exploiting regenerative structure and applying the RM lead to improvements over other methods.

This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is 3/5(1). Introduction to Simulation: Programming Techniques and Methods Analysis McGraw-Hill Computer Science Series McGraw-Hill Series in Marketing COMPUTER SCIENCES SERIES: Author: James Andrew Payne: Edition: illustrated: Publisher: McGraw-Hill, Original from: the University of Michigan: Digitized: ISBN:   In this paper, a Regenerative Adaptive Subset Simulation (RASS) method is proposed to overcome the limitations of the original subset simulation.

The proposed modifications include a more efficient advanced Markov Chain sample generation procedure and an adaptive algorithm to improve the convergence and the stability of the method.

7 Multivariate Output Analysis 88 8 Small-Sample Theory 90 9 Simulations Driven by Empirical Distributions 91 10 The Simulation Budget 93 IV Steady-State Simulation 96 1 Introduction 96 2 Formulas for the Bias and Variance 3 Variance Estimation for Stationary Processes 4 The Regenerative Method 5 The Method of Batch Means This book provides a self-contained introduction to the simulation of flow and transport in porous media, written by a developer of numerical methods.

The reader will learn how to implement reservoir simulation models and computational algorithms in a robust and efficient manner. The Big Book of Simulation Modeling: Multimethod Modeling with Anylogic 6. Amazon. CACI Products Company Statistical Techniques for Simulation Output Analysis Crane, M.A.

and Lemoine, A.J. An Introduction to the Regenerative Method for Simulation Analysis. Lecture Notes in Control and Information Sciences. Vol. Springer. The Introduction to Stress Analysis Applications with SolidWorks Simulation and its supporting materials is designed to assist you in learning SolidWorks Simulation in an academic setting.

steady-state simulation output analysis came from Cox and Smith (),and the idea was further developed in Kabak (). However, the first compre-hensive development of the regenerative method for steady-state simulation output analysis came in a series of papersof Crane and Iglehart(a,b, ), as well as concurrent work by Fishman.

forward the model of regenerative braking and analyzed the braking performance [6–10]. However, only the simulation results are presented, not the experimental results, which is not convincing.

Furthermore, the method of improving energy recovery efficiency was validated. Online Version Only. Book made by this file is ILLEGAL. CONTENTS 1 INTRODUCTION 4 What is Simulation.

4 Time-Oriented Simulation versus Discrete Event Simulation 5 Hints for Using the Tutorial 6 Overview of the Tutorial 6 2 OVERVIEW OF PLANT SIMULATION 8 Object Orientation 8 The Desktop 10 Working with the Class Library and Toolbox 11 Overview of Basic Objects 12 Objects used in this Tutorial The standard regenerative method has a smaller variance constant than does the alternative.

1 Introduction Simulation is frequently used to estimate performance measures of complex stochastic systems which defy solution by classical methods of analysis.

These measures are generally of two kinds: transient (also called terminating) and steady-state. This book is devoted to the most used methodologies for performance evaluation: simulation using specialized software and mathematical modeling.

An important part is dedicated to the simulation, particularly in its theoretical framework and the precautions to be taken in the implementation of the experimental procedure. These principles are illustrated by concrete examples achieved through. Simulation optimization is an area that has attracted the attention of many researchers.

The six major categories of simulation optimization methods are displayed in Figure 3. Section 2 contains brief descriptions of frequently used simulation optimization methods. Section 3 enumerates the reported applications of simulation optimization.

7 Multivariate Output Analysis 88 8 Small-Sample Theory 90 9 Simulations Driven by Empirical Distributions 91 10 The Simulation Budget 93 IV St eady-State Simulation 96 1 Introduction 96 2 Formulas for the Bias and Variance 3 Variance Estimation for Stationary Processes 4 The Regenerative Method 5 The Method of Batch Means.

Uses Finite Element Analysis (FEA) as Implemented in SolidWorks Simulation. Outlining a path that readers can follow to ensure a static analysis that is both accurate and sound, Introduction to Static Analysis using SolidWorks Simulation effectively applies one of the most widely used software packages for engineering design to the concepts of static analysis.

A control system of regenerative braking needs to be established for the simulation and analysis of the regenerative braking procedure. In this study, the model of vehicle dynamics, the tires, the electric motor, the battery and the braking control unit are set up in MATLAB/Simulink.Performing Simulation Analysis.

Following are the steps to perform simulation analysis. Step 1 − Prepare a problem statement. Step 2 − Choose input variables and create entities for the simulation process.

There are two types of variables - decision variables and uncontrollable variables.