Atnaujinkite slapukų nuostatas

El. knyga: Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies

3.50/5 (4 ratings by Goodreads)
  • Formatas: EPUB+DRM
  • Išleidimo metai: 22-Feb-2013
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781118527443
Kitos knygos pagal šią temą:
Advanced Dynamic-System Simulation: Model Replication and Monte Carlo StudiesDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Išleidimo metai: 22-Feb-2013
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781118527443
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

"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.
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97811185274436e.html
Raktažodžiai: