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Modelling and Simulation: Exploring Dynamic System Behaviour Third Edition 2019 [Kõva köide]

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  • Formaat: Hardback, 551 pages, kõrgus x laius: 235x155 mm, kaal: 1016 g, 31 Illustrations, color; 298 Illustrations, black and white; XVIII, 551 p. 329 illus., 31 illus. in color., 1 Hardback
  • Sari: Simulation Foundations, Methods and Applications
  • Ilmumisaeg: 02-Jan-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 303018868X
  • ISBN-13: 9783030188689
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Modelling and Simulation: Exploring Dynamic System Behaviour Third Edition 2019Suurem pilt
  • Formaat: Hardback, 551 pages, kõrgus x laius: 235x155 mm, kaal: 1016 g, 31 Illustrations, color; 298 Illustrations, black and white; XVIII, 551 p. 329 illus., 31 illus. in color., 1 Hardback
  • Sari: Simulation Foundations, Methods and Applications
  • Ilmumisaeg: 02-Jan-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 303018868X
  • ISBN-13: 9783030188689
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030188689.html

This hands-on textbook/reference presents an introduction to the fundamental aspects of modelling and simulation, both for those wishing to learn about this methodology and also for those who have a need to apply it in their work. The text is supported by illustrative examples, drawn from projects formulated within the domains of discrete-event dynamic systems (DEDS) and continuous-time dynamic systems (CTDS).

This updated new edition has been enhanced with new illustrative case studies, and additional examples demonstrating some new features and the effectiveness of the ABCmod conceptual modelling framework.  Changes that facilitate the development of simulation models with ABSmod/J are illustrated.  New material includes a presentation of the experimentation strategy called “design of experiments” and three new chapters that explore the optimization-simulation interface.

Topics and features: presents a goal-based and project-oriented perspective of  modelling and simulation; describes the ABCmod framework, an activity-based conceptual modelling framework for DEDS; examines the simulation-optimization interface in both the CTDS and DEDS domains; provides  numerous illustrative examples, case studies  and useful algorithms, as well as  exercises and projects at the end of most chapters; includes appendices on probability and statistics, the GPSS programming environment, and relevant MATLAB features; provides supplementary software and teaching support material at an associated website, including lecture slides and a methodology for organizing student projects.

Serving as an essential guide to the foundations of modelling and simulation, this practical primer is ideal for senior undergraduate and junior graduate-level students. Also suitable for self-study, the book will be of great benefit to professionals seeking insight into the vast potential of this rapidly evolving problem-solving paradigm.



This book provides an essential foundation in Modelling and Simulation, intended for students in introductory courses, and for self-study by professionals and others interested in the features and potential of this rapidly evolving problem-solving paradigm.
Part I Fundamentals
1 Introduction
3(16)
1.1 Opening Perspectives
3(2)
1.2 Role of Modelling and Simulation
5(1)
1.3 The Nature of a Model
6(2)
1.4 An Example Project (Full-Service Gas Station)
8(2)
1.5 Is There a Downside to the Modelling and Simulation Paradigm?
10(1)
1.6 Monte Carlo Simulation
11(2)
1.7 Simulators
13(1)
1.8 Historical Overview
14(2)
1.9 Exercises and Projects
16(1)
References
17(2)
2 Modelling and Simulation Fundamentals
19(40)
2.1 Some Reflections on Models
19(2)
2.2 Exploring the Foundations
21(14)
2.2.1 The Observation Interval
21(2)
2.2.2 Entities and Their Interactions
23(1)
2.2.3 Data Requirements
24(2)
2.2.4 Constants and Parameters
26(1)
2.2.5 Time and Other Variables
26(6)
2.2.6 An Example: The Bouncing Ball
32(3)
2.3 The Modelling and Simulation Process
35(7)
2.3.1 The Problem Description
35(2)
2.3.2 The Project Goal
37(1)
2.3.3 The Conceptual Model
38(1)
2.3.4 The Simulation Model
39(1)
2.3.5 The Simulation Program
40(1)
2.3.6 The Operational Phases
41(1)
2.4 Verification and Validation
42(4)
2.5 Quality Assurance
46(3)
2.5.1 Documentation
46(1)
2.5.2 Program Development Standards
47(1)
2.5.3 Testing
47(1)
2.5.4 Experiment Design
48(1)
2.5.5 Presentation/Interpretation of Results
49(1)
2.6 The Dynamic Model Landscape
49(2)
2.6.1 Deterministic and Stochastic
49(1)
2.6.2 Discrete and Continuous
50(1)
2.6.3 Linear and Non-linear
51(1)
2.7 Exercises and Projects
51(1)
References
52(7)
Part II DEDS Modelling and Simulation
3 DEDS Stochastic Behaviour and Modelling
59(44)
3.1 The Stochastic Nature of DEDS Models
59(4)
3.2 DEDS-Specific Variables
63(6)
3.2.1 Random Variates and RVVs
63(1)
3.2.2 Entities and Their Attributes
64(2)
3.2.3 Discrete-Time Variables
66(3)
3.3 Input
69(3)
3.3.1 Modelling Exogenous Input
71(1)
3.3.2 Modelling Endogenous Input
71(1)
3.4 Output
72(3)
3.5 DEDS Modelling and Simulation Studies
75(1)
3.6 Data Modelling
76(17)
3.6.1 Defining Data Models Using Collected Data
76(1)
3.6.2 Does the Collected Data Belong to a Homogeneous Stochastic Process?
77(4)
3.6.3 Fitting a Distribution to Data
81(9)
3.6.4 Empirical Distributions
90(2)
3.6.5 Data Modelling with No Data
92(1)
3.7 Simulating Random Behaviour
93(8)
3.7.1 Random Number Generation
93(3)
3.7.2 Random Variate Generation
96(5)
References
101(2)
4 A Conceptual Modelling Framework for DEDS
103(52)
4.1 Introduction
103(8)
4.1.1 Exploring Structural and Behavioural Requirements
105(5)
4.1.2 Overview of the Constituents of the ABCmod Framework
110(1)
4.2 Essential Features of an ABCmod Conceptual Model
111(12)
4.2.1 Model Structure
111(5)
4.2.2 Model Behaviour
116(7)
4.3 Conceptual Modelling in the ABCmod Framework
123(20)
4.3.1 Project Goal: Parameters, Experimentation and Output
123(1)
4.3.2 High-Level ABCmod Conceptual Model
124(10)
4.3.3 Detailed ABCmod Conceptual Model
134(9)
4.4 Examples of ABCmod Conceptual Modelling: A Preview
143(3)
4.5 Exercises and Projects
146(6)
References
152(3)
5 DEDS Simulation Model Development
155(38)
5.1 Constructing a Simulation Model
155(1)
5.2 The Traditional World Views
156(8)
5.2.1 The Activity Scanning World View
157(1)
5.2.2 The Event Scheduling World View
158(1)
5.2.3 The Three-Phase World View
159(2)
5.2.4 The Process-Oriented World View
161(3)
5.3 Transforming an ABCmod Conceptual Model into a Three-Phase Simulation Model
164(12)
5.4 Transforming an ABCmod Conceptual Model into a Process-Oriented Simulation Model
176(15)
5.4.1 Process-Oriented Simulation Models
176(1)
5.4.2 Overview of GPSS
177(3)
5.4.3 Developing a GPSS Simulation Model from an ABCmod Conceptual Model
180(11)
5.5 Exercises and Projects
191(1)
References
191(2)
6 The Activity-Object World View for DEDS
193(42)
6.1 Building Upon the Object-Oriented Programming Paradigm
193(1)
6.2 Implementing an ABCmod Conceptual Model Within the Activity-Object World View
194(4)
6.2.1 Overview
194(2)
6.2.2 Execution and Time Advance Algorithm
196(2)
6.3 ABSmod/J
198(13)
6.3.1 The AOSimulationModel Class
198(6)
6.3.2 Equivalents to ABCmod Structural Components
204(1)
6.3.3 Equivalents to ABCmod Procedures
204(1)
6.3.4 Behaviour Objects
205(2)
6.3.5 Bootstrapping and Evaluating Preconditions
207(1)
6.3.6 Output
207(2)
6.3.7 Summary
209(2)
6.4 Examples of Activity-Object Simulation Models
211(20)
6.4.1 Kojo's Kitchen
211(10)
6.4.2 Port Version 2: Selected Features
221(7)
6.4.3 Advantages of Using Entity Categories with scope = Many[ N
228(3)
6.5 Closing Comments
231(1)
6.6 Exercises and Projects
232(1)
References
232(3)
7 Experimentation and Output Analysis
235(48)
7.1 Overview of the Issue
235(4)
7.2 Bounded Horizon Studies
239(5)
7.2.1 Point Estimates
239(1)
7.2.2 Interval Estimation
240(1)
7.2.3 Output Analysis for the Kojo's Kitchen Project
241(3)
7.3 Steady-State Studies
244(11)
7.3.1 Determining the Warm-up Period
245(5)
7.3.2 Collection and Analysis of Results
250(2)
7.3.3 Experimentation and Data Analysis for the Port Project (Version 1)
252(3)
7.4 Comparing Alternatives
255(7)
7.4.1 Comparing Two Alternatives
256(4)
7.4.2 Comparing Three or More Alternatives
260(2)
7.5 Design of Experiments
262(16)
7.5.1 Introduction
262(5)
7.5.2 Examples of the Design of Experiment (DoE) Methodology
267(11)
7.6 Exercises and Projects
278(1)
References
279(4)
Part III CTDS Modelling and Simulation
8 Modelling of Continuous-Time Dynamic Systems
283(22)
8.1 Introduction
283(1)
8.2 Some Examples of CTDS Conceptual Models
284(7)
8.2.1 Simple Electrical Circuit
284(1)
8.2.2 Automobile Suspension System
285(2)
8.2.3 Fluid Level Control
287(1)
8.2.4 Population Dynamics
288(3)
8.3 Safe Ejection Envelope: A Case Study
291(7)
8.4 State-Space Representation
298(6)
8.4.1 The Canonical Form
298(2)
8.4.2 The Transformation Process
300(4)
References
304(1)
9 Simulation with CTDS Models
305(40)
9.1 Overview of the Numerical Solution Process
305(7)
9.1.1 The Initial Value Problem
305(1)
9.1.2 Existence Theorem for the rVP
306(1)
9.1.3 What Is the Numerical Solution to an IVP?
307(2)
9.1.4 Comparison of Two Preliminary Methods
309(3)
9.2 Some Families of Solution Methods
312(3)
9.2.1 The Runge--Kutta Family
312(1)
9.2.2 The Linear Multistep Family
313(2)
9.3 The Variable Step-Size Process
315(3)
9.4 Circumstances Requiring Special Care
318(10)
9.4.1 Stability
318(2)
9.4.2 Stiffness
320(3)
9.4.3 Discontinuity
323(5)
9.4.4 Concluding Remarks
328(1)
9.5 Options and Choices in CTDS Simulation Software
328(1)
9.6 The Safe Ejection Envelope Project Revisited
329(5)
9.7 Exercises and Projects
334(5)
References
339(6)
Part IV Simulation Optimization
10 Optimization Overview
345(14)
10.1 Introduction
345(1)
10.2 Methods for Unconstrained Minimization
346(9)
10.2.1 Gradient-Dependent Methods
347(5)
10.2.2 Heuristic Methods
352(3)
10.3 Exercises and Projects
355(2)
References
357(2)
11 Simulation Optimization in the CTDS Domain
359(10)
11.1 Introduction
359(1)
11.2 Problem Statement
359(1)
11.3 Some Representative Forms for the Criterion Function
360(1)
11.4 Using Gradient Dependent Methods
361(1)
11.5 An Application in Optimal Control
362(2)
11.6 Dealing with Computational Overhead
364(3)
11.7 Exercises and Projects
367(1)
References
368(1)
12 Simulation Optimization in the DEDS Domain
369(12)
12.1 Introduction
369(1)
12.2 Problem Statement
369(2)
12.3 Overview of Search Strategies
371(2)
12.4 A Case Study (ACME Manufacturing)
373(5)
12.4.1 Problem Outline
373(2)
12.4.2 Experimentation
375(3)
References
378(3)
Annex 1 ABCmod Applications in M&S Projects 381(70)
Annex 2 Probability and Statistics Primer 451(40)
Annex 3 GPSS Primer 491(30)
Annex 4 MATLAB Primer 521(26)
Index 547
Louis G. Birta is Professor Emeritus at the University of Ottawa, School of Electrical Engineering and Computer Science.

Gilbert Arbez is an Assistant Professor in the School of Electrical Engineering and Computer Science, at the University of Ottawa, ON, Canada.