计算机辅助建模技术（影印版）

　　计算机辅助仿真一书从概念到程序上阐述了从基础系统到高级复杂系统中基于Matlab语言仿真模拟技术。本书包含计算机辅助模拟设计介绍，物理系统建模仿真，基础的计算机仿真建模实例，高级仿真，仿真结果的方法性描述等内容。本书除了大量使用Matlab实例以外，还使用基本规则对物理系统建模并推导出系统方程，系统建模可以使研究人员根据需要的算法优化代码，并且直观的得到仿真的结果。本书适用于系统仿真领域的大学及研究所的相关研究人员使用，研究生及高年级本科生也可以作为教材使用。

暂缺《计算机辅助建模技术（影印版）》作者简介

Preface
1.Introduction to System Simulations
1.1  Introduction
  1.1.1  Model Design
  1.1.2  Model Execution
  1.1.3  Model Analysis through Outputs
1.2  Dynamic System Modeling
  1.2.I  Classification of DynamicSystems
1.3  Basic System Elements
  1.3.1  Elements in Electrical Systems
  1.3.2  Resistor
  1.3.3  Capacitor
  1.3.4  Inductor
1.4  Elements in Mechanical Systems
  1.4.1  Springs
  1.4.2  Viscous Damper
  1.4.3  Mass
  1.4.4  Torque-Angular Velocity on aSpring
  1.4.5  Viscous Damper
  1.4.6  Inertia
  1.4.7  Gear Train
1.5  Laplace Transform
1.6  Combined Elements
  1.6.1  Example 1: RLC Circuit
  1.6.2  Example 2: Spring， Mass and DamperSystem
  1.6.3  Example 3: Electrical Network
1.7  Nonlinear Elements
1.8  Thermal and Chemical Systems
  1.8.1  Thermal Systems
  1.8.2  Chemical Systems
1.9  Hydraulic Systems
I.10  Other Formulas
References
2.Creating Your Simulation Environment
2.1 Hardware and Software
  2.1.1  Hardware for Installing Matlab
  2.1.2  Features of Matlab Software
2.2  Matlab Basics
  2.2.1  Getting Help
  2.2.2  Variables
  2.2.3  Assignment Statement
  2.2.4  Special Variables
  2.2.5  Commands Involving Variables
  2.2.6  Matrices
  2.2.7  Matrix Operations
  2.2.8  Matrix Properties
  2.2.9  Programming: Flow ControlStructures
  2.2.10  Flow Control and Loops
  2.2.11  Elementary Functions
2.3  Basics on Computer Simulations
  2.3.I  Creating Data Files
  2.3.2  Write Data on a File
  2.3.3  Loading a Data File
  2.3.4  Deleting Rows and Columns
  2.3.5  Long Command Lines
  2.3.6  Vectorization
  2.3.7  Making Your Own Functions
2.4  Memory Management in Simulations
2.5  Customized Functions
2.6  Computer Simulations with Other Languages
References
3.Data Visualization
3.1  Elementary X-Y Graphs
  3.1.1  Two-Dimensional Graphs
  3.1 .2  Multiple Plots and Curves
  3.1.3  Polynomials
  3.1.4  Plotting Complex Numbers
  3.1.5  Axis Scaling
  3.1.6  Polar Plots
  3.1.7  Discrete Sequence Plot
  3.1.8  Multiple Graphs
3.2  Elementary X-Y-Z Graphs
  3.2.1  Three-Dimensional Basic Plots
  3.2.2  Three-Dimensional Color Plots
  3.2.3  Discrete Sequence Plot
  3.2.4  Polar Plots
  3.2.5  Creating Surfaces
  3.2.6  Multiple Axes for DifferentScaling
  3.2.7  Titles with Symbols
  3.2.8  Special Graphs
  3.2.9  Lines as Ribbons
  3.2.10  Contour Plot
3.3  Other Software Tools
References
4.Examples of Different Systems
4.1  Chaotic System I : Lorenz Attractor
4.2  Chaotic System 2: The Henon-Map
4.3  Two-link Robot Manipulator
4.4  Nonlinear Systems: State Portrait
4.5  Mechanical Systems: Two Mass-spring CartSystem
4.6  Electrical Systems: L-R-C Circuits
4.7  Electro-Mechanical Systems: Magnetically SuspendedBall
4.8  Nonlinear Systems: Pendulum
4.9  Pneumatic Artificial Muscles （PAM）
4.10  Duffing'sEquation
4.11  Two-Mass Flexible Rod
4.12  Mobile Robot Model
References
5.Systems Identification and Modeling
5.1  System Identification
  5.1.I  Introduction
  5.1.2  Steps of System Identification
  5.1.3  System Identification in Matlab
5.2  System Modeling
  5.2.1  Systems Control Approaches
  5.2.2  DC Motor Controller
  5.2.3  Further Analysis on MagneticallySuspended Ball
  5.2.4  Inverted Pendulum
  5.2.5  Pneumatic Muscle Arm
References
6.System Integration
6.1  Integration of Components
6.2  Matlab Simulink
6.3  Basic Simulations
6.4  Data Capturing
6.5  Analysis in the S-domain
6.6  State Space Models
6.7  Integration of Logical Blocks
6.8  Integration of Electrical Elements
References
7.Advanced Simulations
7.1  Introduction
7.2  Genetic Algorithms
  7.2.I  Biological Analogy
  7.2.2  Algorithm
  7.2.3  Binary Encoding
  7.2.4  Selection
  7.2.5  Genetic Operators
  7.2.6  Example I
7.3  Genetic Algorithm Based Cable Selection
  7.3.1  Background
  7.3.2  Cable Selection\
  7.3.3  Genetic Algorithm Based CableSelection
7.4  Advanced Implementation Tools
  7.4.I  Calling Built in Function in CommandLine
  7.4.2  Using the Genetic Algorithm Tool
7.5  Additionl Examples
  7.5.I  Rastrigin's Function
  7.5.2  Schwefel's Function
  7.5.3  Griewangk's Function
References
8.Modeling of Complex Systems
8.1  Complex Systems
8.2  Case Study
8.3  Neural Network Model
  8.3.1  General Framework for ANN Model
  8.3.2  Modeling Generator Loads
8.4  Data and System Training
  8.4.1  Training Model 2
  8.4.2  Model 2 Performance
8.5  Maximizing Power Generation
8.6  Power and Energy Calculations
8.7  Remarks
  8.7.1  Economical Benefits
  8.7.2  Soft Computing Approaches
References
9.Selecting The Right Software Tool
9.1  Scientific Software Tools and Matlab Toolboxes
9.2  Image Processing Based Application
  9.2.i  Introduction
  9.2.2  System Development
  9.2.3  Image Analysis
  9.2.4  First Reasoning Criterion
  9.2.5  Second Reasoning Criterion
  9.2.6  How to Use Criteria？
  9.2.7  Detection of Illumination LevelVariation
  9.2.8  Image Enhancement
9.3  Setting Up the System
  9.3.1  Setup Program 1
  9.3.2  Setup Program 2
  9.3.3  White Reference Program
9.4  Image Acquisition and Processing
  9.4.1  Acquisition Algorithm
  9.4.2  Image Processing （Main）Algorithm
9.5  Experimental Results
  9.5.1  Intensity Variation
  9.5.2  Object Identification
References
10.Exercises
10.i  Matrices
10.2  Graphics
10.3  Analytical Solutions
10.4  Transformations
10.5  Modeling and Forecasting
Index