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E-raamat: Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies 2nd Edition [Wiley Online]

  • Formaat: 280 pages
  • Ilmumisaeg: 03-May-2013
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118527410
  • ISBN-13: 9781118527412
Teised raamatud teemal:
  • Wiley Online
  • Hind: 124,76 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies 2nd EditionSuurem pilt
  • Formaat: 280 pages
  • Ilmumisaeg: 03-May-2013
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118527410
  • ISBN-13: 9781118527412
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118527412
"This book introduces Dynamic-system Simulation with a main emphasis on OPEN DESIRE and DESIRE software"--

"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 Networks, and CLEARN Algorithm. In addition, a CD will be packaged with each book, containing complete binary OPEN DESIRE modeling/simulation program packages for personal-computer LINUX and MS Windows, DESIRE examples, source code and a comprehensive, indexed reference manual. The second edition offers a complete update of all material, boasting two completely new chapters on fast simulation of neural networks"--



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 Networks, and CLEARN Algorithm. In addition, a CD will be packaged with each book, containing complete binary OPEN DESIRE modeling/simulation program packages for personal-computer LINUX and MS Windows, DESIRE examples, source code and a comprehensive, indexed reference manual. The second edition offers a complete update of all material, boasting two completely new chapters on fast simulation of neural networks.
Preface xiii
Chapter 1 Dynamic-System Models And Simulation
1(30)
Simulation Is Experimentation With Models
1(1)
1-1 Simulation and Computer Programs
1(1)
1-2 Dynamic-System Models
2(1)
(a) Difference-Equation Models
2(1)
(b) Differential-Equation Models
2(1)
(c) Discussion
3(1)
1-3 Experiment Protocols Define Simulation Studies
3(1)
1-4 Simulation Software
4(1)
1-5 Fast Simulation Program for Interactive Modeling
5(3)
Anatomy Of A Simulation Run
8(1)
1-6 Dynamic-System Time Histories Are Sampled Periodically
8(2)
1-7 Numerical Integration
10(1)
(a) Euler Integration
10(1)
(b) Improved Integration Rules
10(1)
1-8 Sampling Times and Integration Steps
11(1)
1-9 Sorting Defined-Variable Assignments
12(1)
Simple Application Programs
12(1)
1-10 Oscillators and Computer Displays
12(3)
(a) Linear Oscillator
12(2)
(b) Nonlinear Oscillator: Duffing's Differential Equation
14(1)
1-11 Space-Vehicle Orbit Simulation with Variable-Step Integration
15(2)
1-12 Population-Dynamics Model
17(1)
1-13 Splicing Multiple Simulation Runs: Billiard-Ball Simulation
17(4)
Inroduction To Control-System Simulation
21(1)
1-14 Electrical Servomechanism with Motor-Field Delay and Saturation
21(2)
1-15 Control-System Frequency Response
23(1)
1-16 Simulation of a Simple Guided Missile
24(4)
(a) Guided Torpedo
24(2)
(b) Complete Torpedo-Simulation Program
26(2)
Stop And Look
28(1)
1-17 Simulation in the Real World: A Word of Caution
28(3)
References
29(2)
Chapter 2 Models With Difference Equations, Limiters, And Switches
31(26)
Sampled-Data Systems And Difference Equations
31(1)
2-1 Sampled-Data Difference-Equation Systems
31(1)
(a) Introduction
31(1)
(b) Difference Equations
31(1)
(c) A Minefield of Possible Errors
32(1)
2-2 Solving Systems of First-Order Difference Equations
32(3)
(a) General Difference-Equation Model
32(1)
(b) Simple Recurrence Relations
33(2)
2-3 Models Combining Differential Equations and Sampled-Data Operations
35(1)
2-4 Simple Example
35(1)
2-5 Initializing and Resetting Sampled-Data Variables
35(2)
Two Mixed Continuous/Sampled-Data Systems
37(1)
2-6 Guided Torpedo with Digital Control
37(1)
2-7 Simulation of a Plant with a Digital PID Controller
37(3)
Dynamic-System Models With Limiters And Switches
40(1)
2-8 Limiters, Switches, and Comparators
40(3)
(a) Limiter Functions
40(2)
(b) Switching Functions and Comparators
42(1)
2-9 Integration of Switch and Limiter Outputs, Event Prediction, and Display Problems
43(1)
2-10 Using Sampled-Data Assignments
44(1)
2-11 Using the step Operator and Heuristic Integration-Step Control
44(1)
2-12 Example: Simulation of a Bang-Bang Servomechanism
45(1)
2-13 Limiters, Absolute Values, and Maximum/Minimum Selection
46(1)
2-14 Output-Limited Integration
47(1)
2-15 Modeling Signal Quantization
48(1)
Efficient Device Models Using Recursive Assignments
48(1)
2-16 Recursive Switching and Limiter Operations
48(1)
2-17 Track/Hold Simulation
49(1)
2-18 Maximum-Value and Minimum-Value Holding
50(1)
2-19 Simple Backlash and Hysteresis Models
51(1)
2-20 Comparator with Hysteresis (Schmitt Trigger)
52(1)
2-21 Signal Generators and Signal Modulation
53(4)
References
55(2)
Chapter 3 Fast Vector-Matrix Operations And Submodels
57(20)
Arrays, Vectors, And Matrices
57(1)
3-1 Arrays and Subscripted Variables
57(1)
(a) Improved Modeling
57(1)
(b) Array Declarations, Vectors, and Matrices
57(1)
(c) State-Variable Declarations
58(1)
3-2 Vector and Matrices in Experiment Protocols
58(1)
3-3 Time-History Arrays
58(1)
Vectors And Model Replication
59(1)
3-4 Vector Operations in DYNAMIC Program Segments: The Vectorizing Compiler
59(2)
(a) Vector Assignments and Vector Expressions
59(1)
(b) Vector Differential Equations
60(1)
(c) Vector Sampled-Data Assignments and Difference Equations
60(1)
3-5 Matrix-Vector Products in Vector Expressions
61(2)
(a) Definition
61(1)
(b) Simple Example: Resonating Oscillators
61(2)
3-6 Index-Shift Operation
63(1)
(a) Definition
63(1)
(b) Preview of Significant Applications
63(1)
3-7 Sorting Vector and Subscripted-Variable Assignments
64(1)
3-8 Replication of Dynamic-System Models
64(1)
More Vector Operations
65(1)
3-9 Sums, DOT Products, and Vector Norms
65(1)
(a) Sums and DOT Products
65(1)
(b) Euclidean, Taxicab, and Hamming Norms
65(1)
3-10 Maximum/Minimum Selection and Masking
66(1)
(a) Maximum/Minimum Selection
66(1)
(b) Masking Vector Expressions
66(1)
Vector Equivalence Declarations Simplify Models
67(1)
3-11 Subvectors
67(1)
3-12 Matrix-Vector Equivalence
67(1)
Matrix Operations In Dynamic-System Models
67(1)
3-13 Simple Matrix Assignments
67(1)
3-14 Two-Dimensional Model Replication
68(1)
(a) Matrix Expressions and DOT Products
68(1)
(b) Matrix Differential Equations
68(1)
(c) Matrix Difference Equations
69(1)
Vectors In Physics And Control-System Problems
69(1)
3-15 Vectors in Physics Problems
69(1)
3-16 Vector Model of a Nuclear Reactor
69(1)
3-17 Linear Transformations and Rotation Matrices
70(2)
3-18 State-Equation Models of Linear Control Systems
72(1)
User-Defined Functions And Submodels
72(1)
3-19 Introduction
72(1)
3-20 User-Defined Functions
72(1)
3-21 Submodel Declaration and Invocation
73(2)
3-22 Dealing with Sampled-Data Assignments, Limiters, and Switches
75(2)
References
75(2)
Chapter 4 Efficient Parameter-Influence Studies And Statistics Computation
77(32)
Model Replication Simplifies Parameter-Influence Studies
77(1)
4-1 Exploring the Effects of Parameter Changes
77(1)
4-2 Repeated Simulation Runs Versus Model Replication
78(2)
(a) Simple Repeated-Run Study
78(1)
(b) Model Replication (Vectorization)
78(2)
4-3 Programming Parameter-Influence Studies
80(4)
(a) Measures of System Performance
80(1)
(b) Program Design
81(1)
(c) Two-Dimensional Model Replication
81(1)
(d) Cross-Plotting Results
82(1)
(e) Maximum/Minimum Selection
83(1)
(f) Iterative Parameter Optimization
83(1)
Statistics
84(1)
4-4 Random Data and Statistics
84(1)
4-5 Sample Averages and Statistical Relative Frequencies
85(1)
Computing Statistics By Vector Averaging
85(1)
4-6 Fast Computation of Sample Averages
85(1)
4-7 Fast Probability Estimation
86(1)
4-8 Fast Probability-Density Estimation
86(4)
(a) Simple Probability-Density Estimate
86(1)
(b) Triangle and Parzen Windows
87(1)
(c) Computation and Display of Parzen-Window Estimates
88(2)
4-9 Sample-Range Estimation
90(1)
Replicated Averages Generate Sampling Distributions
91(1)
4-10 Computing Statistics by Time Averaging
91(1)
4-11 Sample Replication and Sampling-Distribution Statistics
91(4)
(a) Introduction
91(2)
(b) Demonstrations of Empirical Laws of Large Numbers
93(2)
(c) Counterexample: Fat-Tailed Distribution
95(1)
Random-Process Simulation
95(1)
4-12 Random Processes and Monte Carlo Simulation
95(2)
4-13 Modeling Random Parameters and Random Initial Values
97(1)
4-14 Sampled-Data Random Processes
97(1)
4-15 "Continuous" Random Processes
98(2)
(a) Modeling Continuous Noise
98(1)
(b) Continuous Time Averaging
99(1)
(c) Correlation Functions and Spectral Densities
100(1)
4-16 Problems with Simulated Noise
100(1)
Simple Monte Carlo Experiments
100(1)
4-17 Introduction
100(1)
4-18 Gambling Returns
100(2)
4-19 Vectorized Monte Carlo Study of a Continuous Random Walk
102(7)
References
106(3)
Chapter 5 Monte Carlo Simulation Of Real Dynamic Systems
109(18)
Introduction
109(1)
5-1 Survey
109(1)
Repeated-Run Monte Carlo Simulation
109(1)
5-2 End-of-Run Statistics for Repeated Simulation Runs
109(1)
5-3 Example: Effects of Gun-Elevation Errors on a 1776 Cannnonball Trajectory
110(3)
5-4 Sequential Monte Carlo Simulation
113(1)
Vectorized Monte Carlo Simulation
113(1)
5-5 Vectorized Monte Carlo Simulation of the 1776 Cannon Shot
113(2)
5-6 Combined Vectorized and Repeated-Run Monte Carlo Simulation
115(1)
5-7 Interactive Monte Carlo Simulation: Computing Runtime Histories of Statistics with DYNAMIC-Segment DOT Operations
115(2)
5-8 Example: Torpedo Trajectory Dispersion
117(2)
Simulation Of Noisy Control Systems
119(1)
5-9 Monte Carlo Simulation of a Nonlinear Servomechanism: A Noise-Input Test
119(2)
5-10 Monte Carlo Study of Control-System Errors Caused by Noise
121(2)
Additional Topics
123(1)
5-11 Monte Carlo Optimization
123(1)
5-12 Convenient Heuristic Method for Testing Pseudorandom Noise
123(1)
5-13 Alternative to Monte Carlo Simulation
123(4)
(a) Introduction
123(1)
(b) Dynamic Systems with Random Perturbations
123(1)
(c) Mean-Square Errors in Linearized Systems
124(1)
References
125(2)
Chapter 6 Vector Models Of Neural Networks
127(50)
Artificial Neural Networks
127(1)
6-1 Introduction
127(1)
6-2 Artificial Neural Networks
127(1)
6-3 Static Neural Networks: Training, Validation, and Applications
128(1)
6-4 Dynamic Neural Networks
129(1)
Simple Vector Assignments Model Neuron Layers
130(1)
6-5 Neuron-Layer Declarations and Neuron Operations
130(1)
6-6 Neuron-Layer Concatenation Simplifies Bias Inputs
130(1)
6-7 Normalizing and Contrast-Enhancing Layers
131(1)
(a) Pattern Normalization
131(1)
(b) Contrast Enhancement: Softmax and Thresholding
131(1)
6-8 Multilayer Networks
132(1)
6-9 Exercising a Neural-Network Model
132(2)
(a) Computing Successive Neuron-Layer Outputs
132(1)
(b) Input from Pattern-Row Matrices
133(1)
(c) Input from Text Files and Spreadsheets
133(1)
SUPERVISED TRAINING FOR REGRESSION
134(1)
6-10 Mean-Square Regression
134(3)
(a) Problem Statement
134(1)
(b) Linear Mean-Square Regression and the Delta Rule
135(1)
(c) Nonlinear Neuron Layers and Activation-Function Derivatives
136(1)
(d) Error-Measure Display
136(1)
6-11 Backpropagation Networks
137(3)
(a) The Generalized Delta Rule
137(2)
(b) Momentum Learning
139(1)
(c) Simple Example
139(1)
(d) The Classical XOR Problem and Other Examples
140(1)
More Neural-Network Models
140(1)
6-12 Functional-Link Networks
140(2)
6-13 Radial-Basis-Function Networks
142(3)
(a) Basis-Function Expansion and Linear Optimization
142(1)
(b) Radial Basis Functions
143(2)
6-14 Neural-Network Submodels
145(1)
Pattern Classification
146(1)
6-15 Introduction
146(1)
6-16 Classifier Input from Files
147(1)
6-17 Classifier Networks
147(2)
(a) Simple Linear Classifiers
147(1)
(b) Softmax Classifiers
148(1)
(c) Backpropagation Classifiers
148(1)
(d) Functional-Link Classifiers
149(1)
(e) Other Classsifiers
149(1)
6-18 Examples
149(6)
(a) Classification Using an Empirical Database: Fisher's Iris Problem
149(2)
(b) Image-Pattern Recognition and Associative Memory
151(4)
Pattern Simplification
155(1)
6-19 Pattern Centering
155(1)
6-20 Feature Reduction
156(1)
(a) Bottleneck Layers and Encoders
156(1)
(b) Principal Components
156(1)
Network-Training Problems
157(1)
6-21 Learning-Rate Adjustment
157(1)
6-22 Overfitting and Generalization
157(2)
(a) Introduction
157(1)
(b) Adding Noise
158(1)
(c) Early Stopping
158(1)
(d) Regularization
159(1)
6-23 Beyond Simple Gradient Descent
159(1)
Unsupervised Competitive-Layer Classifiers
159(1)
6-24 Template-Pattern Matching and the CLEARN Operation
159(4)
(a) Template Patterns and Template Matrix
159(1)
(b) Matching Known Template Patterns
160(1)
(c) Template-Pattern Training
160(2)
(d) Correlation Training
162(1)
6-25 Learning with Conscience
163(1)
6-26 Competitive-Learning Experiments
164(1)
(a) Pattern Classification
164(1)
(b) Vector Quantization
164(1)
6-27 Simplified Adaptive-Resonance Emulation
165(2)
Supervised Competitive Learning
167(1)
6-28 The LVQ Algorithm for Two-Way Classification
167(1)
6-29 Counterpropagation Networks
167(1)
Examples Of Clearn Classifiers
168(1)
6-30 Recognition of Known Patterns
168(5)
(a) Image Recognition
168(1)
(b) Fast Solution of the Spiral Benchmark Problem
169(4)
6-31 Learning Unknown Patterns
173(4)
References
174(3)
Chapter 7 Dynamic Neural Networks
177(30)
Introduction
177(1)
7-1 Dynamic Versus Static Neural Networks
177(1)
7-2 Applications of Dynamic Neural Networks
177(1)
7-3 Simulations Combining Neural Networks and Differential-Equation Models
178(1)
Neural Networks With Delay-Line Input
178(1)
7-4 Introduction
178(2)
7-5 The Delay-Line Model
180(1)
7-6 Delay-Line-Input Networks
180(2)
(a) Linear Combiners
180(1)
(b) One-Layer Nonlinear Network
181(1)
(c) Functional-Link Network
181(1)
(d) Backpropagation Network with Delay-Line Input
182(1)
7-7 Using Gamma Delay Lines
182(1)
Static Neural Networks Used As Dynamic Networks
183(1)
7-8 Introduction
183(1)
7-9 Simple Backpropagation Networks
184(1)
Recurrent Neural Networks
185(1)
7-10 Layer-Feedback Networks
185(1)
7-11 Simplified Recurrent-Network Models Combine Context and Input Layers
185(2)
(a) Conventional Model of a Jordan Network
185(1)
(b) Simplified Jordan-Network Model
186(1)
(c) Simplified Models for Other Feedback Networks
187(1)
7-12 Neural Networks with Feedback Delay Lines
187(2)
(a) Delay-Line Feedback
187(1)
(b) Neural Networks with Both Input and Feedback Delay Lines
188(1)
7-13 Teacher Forcing
189(1)
Predictor Networks
189(1)
7-14 Off-Line Predictor Training
189(3)
(a) Off-Line Prediction Using Stored Time Series
189(1)
(b) Off-Line Training System for Online Predictors
189(1)
(c) Example: Simple Linear Predictor
190(2)
7-15 Online Trainng for True Online Prediction
192(1)
7-16 Chaotic Time Series for Prediction Experiments
192(1)
7-17 Gallery of Predictor Networks
193(6)
Other Applications Of Dynamic Networks
199(1)
7-18 Temporal-Pattern Recognition: Regression and Classification
199(2)
7-19 Model Matching
201(3)
(a) Introduction
201(1)
(b) Example: Program for Matching Narendra's Plant Model
201(3)
Miscellaneous Topics
204(1)
7-20 Biological-Network Software
204(3)
References
204(3)
Chapter 8 More Appications Of Vector Models
207(38)
Vectorized Simulation With Logarithmic Plots
207(1)
8-1 The EUROSIM No. 1 Benchmark Problem
207(1)
8-2 Vectorized Simulation with Logarithmic Plots
207(2)
Modeling Fuzzy-Logic Function Generators
209(1)
8-3 Rule Tables Specify Heuristic Functions
209(1)
8-4 Fuzzy-Set Logic
210(4)
(a) Fuzzy Sets and Membership Functions
210(1)
(b) Fuzzy Intersections and Unions
210(3)
(c) Joint Membership Functions
213(1)
(d) Normalized Fuzzy-Set Partitions
213(1)
8-5 Fuzzy-Set Rule Tables and Function Generators
214(1)
8-6 Simplified Function Generation with Fuzzy Basis Functions
214(1)
8-7 Vector Models of Fuzzy-Set Partitions
215(1)
(a) Gaussian Bumps: Effects of Normalization
215(1)
(b) Triangle Functions
215(1)
(c) Smooth Fuzzy-Basis Functions
216(1)
8-8 Vector Models for Multidimensional Fuzzy-Set Partitions
216(1)
8-9 Example: Fuzzy-Logic Control of a Servomechanism
217(4)
(a) Problem Statement
217(1)
(b) Experiment Protocol and Rule Table
217(3)
(c) DYNAMIC Program Segment and Results
220(1)
Partial Differential Equations
221(1)
8-10 Method of Lines
221(1)
8-11 Vectorized Method of Lines
221(4)
(a) Introduction
221(1)
(b) Using Differentiation Operators
221(3)
(c) Numerical Problems
224(1)
8-12 Heat-Conduction Equation in Cylindrical Coordinates
225(1)
8-13 Generalizations
225(2)
8-14 Simple Heat-Exchanger Model
227(2)
Fourier Analysis And Linear-System Dynamics
229(1)
8-15 Introduction
229(1)
8-16 Function-Table Lookup and Interpolation
230(1)
8-17 Fast-Fourier-Transform Operations
230(1)
8-18 Impulse and Freqency Response of a Linear Servomechanism
231(1)
8-19 Compact Vector Models of Linear Dynamic Systems
232(5)
(a) Using the Index-Shift Operation with Analog Integration
232(3)
(b) Linear Sampled-Data Systems
235(1)
(c) Example: Digital Comb Filter
236(1)
Replication Of Agroecological Models On Map Grids
237(1)
8-20 Geographical Information System
237(2)
8-21 Modeling the Evolution of Landscape Features
239(1)
8-22 Matrix Operations on a Map Grid
239(6)
References
242(3)
APPENDIX: ADDITIONAL REFERENCE MATERIAL
245(3)
A-1 Example of a Radial-Basis-Function Network
245(1)
A-2 Fuzzy-Basis-Function Network
245(3)
References 248(3)
Using The Book CD 251(2)
Index 253
GRANINO A. KORN, PhD, is Professor of Electrical and Computer Engineering at the University of Arizona and a partner with G.A. and T.M. Korn Industrial Consultants, a company that designs systems for interactive simulation of dynamic systems and neural networks. He is the author of fifteen books, a Fellow of the IEEE, and the recipient of several awards for his work on computer simulation.
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