Advanced dynamic-system simulation : model -replication and Monte Carlo Studies /

"This book introduces Dynamic-system Simulation with a main emphasis on OPEN DESIRE and DESIRE software. The book includes eight comprehensive chapters amounting to approximately 250 pages, as well as includes three appendices housing information on Radial-basis-function, Fuzzy-basis-function N...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Korn, Granino A. (Granino Arthur), 1922-2013
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, New Jersey : Wiley, [2013]
Edición:Second edition.
Temas:
System analysis > Simulation methods.
Open source software.
Computer software > Development.
Analyse de systèmes > Méthodes de simulation.
Logiciels libres.
COMPUTERS > Computer Simulation.
Computer software > Development
Open source software
System analysis > Simulation methods
Civil & Environmental Engineering.
Engineering & Applied Sciences.
Operations Research.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: ch. 1 Dynamic-System Models And Simulation
  • Simulation Is Experimentation With Models
  • 1-1. Simulation and Computer Programs
  • 1-2. Dynamic-System Models
  • (a). Difference-Equation Models
  • (b). Differential-Equation Models
  • (c). Discussion
  • 1-3. Experiment Protocols Define Simulation Studies
  • 1-4. Simulation Software
  • 1-5. Fast Simulation Program for Interactive Modeling
  • Anatomy Of A Simulation Run
  • 1-6. Dynamic-System Time Histories Are Sampled Periodically
  • 1-7. Numerical Integration
  • (a). Euler Integration
  • (b). Improved Integration Rules
  • 1-8. Sampling Times and Integration Steps
  • 1-9. Sorting Defined-Variable Assignments
  • Simple Application Programs
  • 1-10. Oscillators and Computer Displays
  • (a). Linear Oscillator
  • (b). Nonlinear Oscillator: Duffing's Differential Equation
  • 1-11. Space-Vehicle Orbit Simulation with Variable-Step Integration
  • 1-12. Population-Dynamics Model
  • 1-13. Splicing Multiple Simulation Runs: Billiard-Ball Simulation
  • Inroduction To Control-System Simulation
  • 1-14. Electrical Servomechanism with Motor-Field Delay and Saturation
  • 1-15. Control-System Frequency Response
  • 1-16. Simulation of a Simple Guided Missile
  • (a). Guided Torpedo
  • (b). Complete Torpedo-Simulation Program
  • Stop And Look
  • 1-17. Simulation in the Real World: A Word of Caution
  • References
  • ch. 2 Models With Difference Equations, Limiters, And Switches
  • Sampled-Data Systems And Difference Equations
  • 2-1. Sampled-Data Difference-Equation Systems
  • (a). Introduction
  • (b). Difference Equations
  • (c). Minefield of Possible Errors
  • 2-2. Solving Systems of First-Order Difference Equations
  • (a). General Difference-Equation Model
  • (b). Simple Recurrence Relations
  • 2-3. Models Combining Differential Equations and Sampled-Data Operations
  • 2-4. Simple Example
  • 2-5. Initializing and Resetting Sampled-Data Variables
  • Two Mixed Continuous/Sampled-Data Systems
  • 2-6. Guided Torpedo with Digital Control
  • 2-7. Simulation of a Plant with a Digital PID Controller
  • Dynamic-System Models With Limiters And Switches
  • 2-8. Limiters, Switches, and Comparators
  • (a). Limiter Functions
  • (b). Switching Functions and Comparators
  • 2-9. Integration of Switch and Limiter Outputs, Event Prediction, and Display Problems
  • 2-10. Using Sampled-Data Assignments
  • 2-11. Using the step Operator and Heuristic Integration-Step Control
  • 2-12. Example: Simulation of a Bang-Bang Servomechanism
  • 2-13. Limiters, Absolute Values, and Maximum/Minimum Selection
  • 2-14. Output-Limited Integration
  • 2-15. Modeling Signal Quantization
  • Efficient Device Models Using Recursive Assignments
  • 2-16. Recursive Switching and Limiter Operations
  • 2-17. Track/Hold Simulation
  • 2-18. Maximum-Value and Minimum-Value Holding
  • 2-19. Simple Backlash and Hysteresis Models
  • 2-20. Comparator with Hysteresis (Schmitt Trigger)
  • 2-21. Signal Generators and Signal Modulation
  • References
  • ch. 3 Fast Vector-Matrix Operations And Submodels
  • Arrays, Vectors, And Matrices
  • 3-1. Arrays and Subscripted Variables
  • (a). Improved Modeling
  • (b). Array Declarations, Vectors, and Matrices
  • (c). State-Variable Declarations
  • 3-2. Vector and Matrices in Experiment Protocols
  • 3-3. Time-History Arrays
  • Vectors And Model Replication
  • 3-4. Vector Operations in DYNAMIC Program Segments: The Vectorizing Compiler
  • (a). Vector Assignments and Vector Expressions
  • (b). Vector Differential Equations
  • (c). Vector Sampled-Data Assignments and Difference Equations
  • 3-5. Matrix-Vector Products in Vector Expressions
  • (a). Definition
  • (b). Simple Example: Resonating Oscillators
  • 3-6. Index-Shift Operation
  • (a). Definition
  • (b). Preview of Significant Applications
  • 3-7. Sorting Vector and Subscripted-Variable Assignments
  • 3-8. Replication of Dynamic-System Models
  • More Vector Operations
  • 3-9. Sums, DOT Products, and Vector Norms
  • (a). Sums and DOT Products
  • (b). Euclidean, Taxicab, and Hamming Norms
  • 3-10. Maximum/Minimum Selection and Masking
  • (a). Maximum/Minimum Selection
  • (b). Masking Vector Expressions
  • Vector Equivalence Declarations Simplify Models
  • 3-11. Subvectors
  • 3-12. Matrix-Vector Equivalence
  • Matrix Operations In Dynamic-System Models
  • 3-13. Simple Matrix Assignments
  • 3-14. Two-Dimensional Model Replication
  • (a). Matrix Expressions and DOT Products
  • (b). Matrix Differential Equations
  • (c). Matrix Difference Equations
  • Vectors In Physics And Control-System Problems
  • 3-15. Vectors in Physics Problems
  • 3-16. Vector Model of a Nuclear Reactor
  • 3-17. Linear Transformations and Rotation Matrices
  • 3-18. State-Equation Models of Linear Control Systems
  • User-Defined Functions And Submodels
  • 3-19. Introduction
  • 3-20. User-Defined Functions
  • 3-21. Submodel Declaration and Invocation
  • 3-22. Dealing with Sampled-Data Assignments, Limiters, and Switches
  • References
  • ch. 4 Efficient Parameter-Influence Studies And Statistics Computation
  • Model Replication Simplifies Parameter-Influence Studies
  • 4-1. Exploring the Effects of Parameter Changes
  • 4-2. Repeated Simulation Runs Versus Model Replication
  • (a). Simple Repeated-Run Study
  • (b). Model Replication (Vectorization)
  • 4-3. Programming Parameter-Influence Studies
  • (a). Measures of System Performance
  • (b). Program Design
  • (c). Two-Dimensional Model Replication
  • (d). Cross-Plotting Results
  • (e). Maximum/Minimum Selection
  • (f). Iterative Parameter Optimization
  • Statistics
  • 4-4. Random Data and Statistics
  • 4-5. Sample Averages and Statistical Relative Frequencies
  • Computing Statistics By Vector Averaging
  • 4-6. Fast Computation of Sample Averages
  • 4-7. Fast Probability Estimation
  • 4-8. Fast Probability-Density Estimation
  • (a). Simple Probability-Density Estimate
  • (b). Triangle and Parzen Windows
  • (c). Computation and Display of Parzen-Window Estimates
  • 4-9. Sample-Range Estimation
  • Replicated Averages Generate Sampling Distributions
  • 4-10. Computing Statistics by Time Averaging
  • 4-11. Sample Replication and Sampling-Distribution Statistics
  • (a). Introduction
  • (b). Demonstrations of Empirical Laws of Large Numbers
  • (c). Counterexample: Fat-Tailed Distribution
  • Random-Process Simulation
  • 4-12. Random Processes and Monte Carlo Simulation
  • 4-13. Modeling Random Parameters and Random Initial Values
  • 4-14. Sampled-Data Random Processes
  • 4-15. "Continuous" Random Processes
  • (a). Modeling Continuous Noise
  • (b). Continuous Time Averaging
  • (c). Correlation Functions and Spectral Densities
  • 4-16. Problems with Simulated Noise
  • Simple Monte Carlo Experiments
  • 4-17. Introduction
  • 4-18. Gambling Returns
  • 4-19. Vectorized Monte Carlo Study of a Continuous Random Walk
  • References
  • ch.
  • 5 Monte Carlo Simulation Of Real Dynamic Systems
  • Introduction
  • 5-1. Survey
  • Repeated-Run Monte Carlo Simulation
  • 5-2. End-of-Run Statistics for Repeated Simulation Runs
  • 5-3. Example: Effects of Gun-Elevation Errors on a 1776 Cannnonball Trajectory
  • 5-4. Sequential Monte Carlo Simulation
  • Vectorized Monte Carlo Simulation
  • 5-5. Vectorized Monte Carlo Simulation of the 1776 Cannon Shot
  • 5-6. Combined Vectorized and Repeated-Run Monte Carlo Simulation
  • 5-7. Interactive Monte Carlo Simulation: Computing Runtime Histories of Statistics with DYNAMIC-Segment DOT Operations
  • 5-8. Example: Torpedo Trajectory Dispersion
  • Simulation Of Noisy Control Systems
  • 5-9. Monte Carlo Simulation of a Nonlinear Servomechanism: A Noise-Input Test
  • 5-10. Monte Carlo Study of Control-System Errors Caused by Noise
  • Additional Topics
  • 5-11. Monte Carlo Optimization
  • 5-12. Convenient Heuristic Method for Testing Pseudorandom Noise
  • 5-13. Alternative to Monte Carlo Simulation
  • (a). Introduction
  • (b). Dynamic Systems with Random Perturbations
  • (c). Mean-Square Errors in Linearized Systems
  • References
  • ch. 6 Vector Models Of Neural Networks
  • Artificial Neural Networks
  • 6-1. Introduction
  • 6-2. Artificial Neural Networks
  • 6-3. Static Neural Networks: Training, Validation, and Applications
  • 6-4. Dynamic Neural Networks
  • Simple Vector Assignments Model Neuron Layers
  • 6-5. Neuron-Layer Declarations and Neuron Operations
  • 6-6. Neuron-Layer Concatenation Simplifies Bias Inputs
  • 6-7. Normalizing and Contrast-Enhancing Layers
  • (a). Pattern Normalization
  • (b). Contrast Enhancement: Softmax and Thresholding
  • 6-8. Multilayer Networks
  • 6-9. Exercising a Neural-Network Model
  • (a). Computing Successive Neuron-Layer Outputs
  • (b). Input from Pattern-Row Matrices
  • (c). Input from Text Files and Spreadsheets
  • SUPERVISED TRAINING FOR REGRESSION
  • 6-10. Mean-Square Regression
  • (a). Problem Statement
  • (b). Linear Mean-Square Regression and the Delta Rule
  • (c). Nonlinear Neuron Layers and Activation-Function Derivatives
  • (d). Error-Measure Display
  • 6-11. Backpropagation Networks
  • (a). Generalized Delta Rule
  • (b). Momentum Learning
  • (c). Simple Example
  • (d). Classical XOR Problem and Other Examples
  • More Neural-Network Models
  • 6-12. Functional-Link Networks
  • 6-13. Radial-Basis-Function Networks
  • (a). Basis-Function Expansion and Linear Optimization
  • (b). Radial Basis Functions
  • 6-14. Neural-Network Submodels.
  • Note continued: Pattern Classification
  • 6-15. Introduction
  • 6-16. Classifier Input from Files
  • 6-17. Classifier Networks
  • (a). Simple Linear Classifiers
  • (b). Softmax Classifiers
  • (c). Backpropagation Classifiers
  • (d). Functional-Link Classifiers
  • (e). Other Classsifiers
  • 6-18. Examples
  • (a). Classification Using an Empirical Database: Fisher's Iris Problem
  • (b). Image-Pattern Recognition and Associative Memory
  • Pattern Simplification
  • 6-19. Pattern Centering
  • 6-20. Feature Reduction
  • (a). Bottleneck Layers and Encoders
  • (b). Principal Components
  • Network-Training Problems
  • 6-21. Learning-Rate Adjustment
  • 6-22. Overfitting and Generalization
  • (a). Introduction
  • (b). Adding Noise
  • (c). Early Stopping
  • (d). Regularization
  • 6-23. Beyond Simple Gradient Descent
  • Unsupervised Competitive-Layer Classifiers
  • 6-24. Template-Pattern Matching and the CLEARN Operation
  • (a). Template Patterns and Template Matrix
  • (b). Matching Known Template Patterns
  • (c). Template-Pattern Training
  • (d). Correlation Training
  • 6-25. Learning with Conscience
  • 6-26. Competitive-Learning Experiments
  • (a). Pattern Classification
  • (b). Vector Quantization
  • 6-27. Simplified Adaptive-Resonance Emulation
  • Supervised Competitive Learning
  • 6-28. LVQ Algorithm for Two-Way Classification
  • 6-29. Counterpropagation Networks
  • Examples Of Clearn Classifiers
  • 6-30. Recognition of Known Patterns
  • (a). Image Recognition
  • (b). Fast Solution of the Spiral Benchmark Problem
  • 6-31. Learning Unknown Patterns
  • References
  • ch. 7 Dynamic Neural Networks
  • Introduction
  • 7-1. Dynamic Versus Static Neural Networks
  • 7-2. Applications of Dynamic Neural Networks
  • 7-3. Simulations Combining Neural Networks and Differential-Equation Models
  • Neural Networks With Delay-Line Input
  • 7-4. Introduction
  • 7-5. Delay-Line Model
  • 7-6. Delay-Line-Input Networks
  • (a). Linear Combiners
  • (b). One-Layer Nonlinear Network
  • (c). Functional-Link Network
  • (d). Backpropagation Network with Delay-Line Input
  • 7-7. Using Gamma Delay Lines
  • Static Neural Networks Used As Dynamic Networks
  • 7-8. Introduction
  • 7-9. Simple Backpropagation Networks
  • Recurrent Neural Networks
  • 7-10. Layer-Feedback Networks
  • 7-11. Simplified Recurrent-Network Models Combine Context and Input Layers
  • (a). Conventional Model of a Jordan Network
  • (b). Simplified Jordan-Network Model
  • (c). Simplified Models for Other Feedback Networks
  • 7-12. Neural Networks with Feedback Delay Lines
  • (a). Delay-Line Feedback
  • (b). Neural Networks with Both Input and Feedback Delay Lines
  • 7-13. Teacher Forcing
  • Predictor Networks
  • 7-14. Off-Line Predictor Training
  • (a). Off-Line Prediction Using Stored Time Series
  • (b). Off-Line Training System for Online Predictors
  • (c). Example: Simple Linear Predictor
  • 7-15. Online Trainng for True Online Prediction
  • 7-16. Chaotic Time Series for Prediction Experiments
  • 7-17. Gallery of Predictor Networks
  • Other Applications Of Dynamic Networks
  • 7-18. Temporal-Pattern Recognition: Regression and Classification
  • 7-19. Model Matching
  • (a). Introduction
  • (b). Example: Program for Matching Narendra's Plant Model
  • Miscellaneous Topics
  • 7-20. Biological-Network Software
  • References
  • ch. 8 More Appications Of Vector Models
  • Vectorized Simulation With Logarithmic Plots
  • 8-1. EUROSIM No. 1 Benchmark Problem
  • 8-2. Vectorized Simulation with Logarithmic Plots
  • Modeling Fuzzy-Logic Function Generators
  • 8-3. Rule Tables Specify Heuristic Functions
  • 8-4. Fuzzy-Set Logic
  • (a). Fuzzy Sets and Membership Functions
  • (b). Fuzzy Intersections and Unions
  • (c). Joint Membership Functions
  • (d). Normalized Fuzzy-Set Partitions
  • 8-5. Fuzzy-Set Rule Tables and Function Generators
  • 8-6. Simplified Function Generation with Fuzzy Basis Functions
  • 8-7. Vector Models of Fuzzy-Set Partitions
  • (a). Gaussian Bumps: Effects of Normalization
  • (b). Triangle Functions
  • (c). Smooth Fuzzy-Basis Functions
  • 8-8. Vector Models for Multidimensional Fuzzy-Set Partitions
  • 8-9. Example: Fuzzy-Logic Control of a Servomechanism
  • (a). Problem Statement
  • (b). Experiment Protocol and Rule Table
  • (c). DYNAMIC Program Segment and Results
  • Partial Differential Equations
  • 8-10. Method of Lines
  • 8-11. Vectorized Method of Lines
  • (a). Introduction
  • (b). Using Differentiation Operators
  • (c). Numerical Problems
  • 8-12. Heat-Conduction Equation in Cylindrical Coordinates
  • 8-13. Generalizations
  • 8-14. Simple Heat-Exchanger Model
  • Fourier Analysis And Linear-System Dynamics
  • 8-15. Introduction
  • 8-16. Function-Table Lookup and Interpolation
  • 8-17. Fast-Fourier-Transform Operations
  • 8-18. Impulse and Freqency Response of a Linear Servomechanism
  • 8-19. Compact Vector Models of Linear Dynamic Systems
  • (a). Using the Index-Shift Operation with Analog Integration
  • (b). Linear Sampled-Data Systems
  • (c). Example: Digital Comb Filter
  • Replication Of Agroecological Models On Map Grids
  • 8-20. Geographical Information System
  • 8-21. Modeling the Evolution of Landscape Features
  • 8-22. Matrix Operations on a Map Grid
  • References
  • APPENDIX: ADDITIONAL REFERENCE MATERIAL
  • A-1. Example of a Radial-Basis-Function Network
  • A-2. Fuzzy-Basis-Function Network.

Ejemplares similares