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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley,
[2013]
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Korn, Granino A. |q (Granino Arthur), |d 1922-2013. | |
| 245 | 1 | 0 | |a Advanced dynamic-system simulation : |b model -replication and Monte Carlo Studies / |c by Granino A. Korn. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Hoboken, New Jersey : |b Wiley, |c [2013] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |a "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"-- |c Provided by publisher. | ||
| 520 | |a "This book introduces Dynamic-system Simulation with a main emphasis on OPEN DESIRE and DESIRE software"-- |c Provided by publisher. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | 0 | |g Machine generated contents note: |g ch. 1 |t Dynamic-System Models And Simulation -- |t Simulation Is Experimentation With Models -- |g 1-1. |t Simulation and Computer Programs -- |g 1-2. |t Dynamic-System Models -- |g (a). |t Difference-Equation Models -- |g (b). |t Differential-Equation Models -- |g (c). |t Discussion -- |g 1-3. |t Experiment Protocols Define Simulation Studies -- |g 1-4. |t Simulation Software -- |g 1-5. |t Fast Simulation Program for Interactive Modeling -- |t Anatomy Of A Simulation Run -- |g 1-6. |t Dynamic-System Time Histories Are Sampled Periodically -- |g 1-7. |t Numerical Integration -- |g (a). |t Euler Integration -- |g (b). |t Improved Integration Rules -- |g 1-8. |t Sampling Times and Integration Steps -- |g 1-9. |t Sorting Defined-Variable Assignments -- |t Simple Application Programs -- |g 1-10. |t Oscillators and Computer Displays -- |g (a). |t Linear Oscillator -- |g (b). |t Nonlinear Oscillator: Duffing's Differential Equation -- |g 1-11. |t Space-Vehicle Orbit Simulation with Variable-Step Integration -- |g 1-12. |t Population-Dynamics Model -- |g 1-13. |t Splicing Multiple Simulation Runs: Billiard-Ball Simulation -- |t Inroduction To Control-System Simulation -- |g 1-14. |t Electrical Servomechanism with Motor-Field Delay and Saturation -- |g 1-15. |t Control-System Frequency Response -- |g 1-16. |t Simulation of a Simple Guided Missile -- |g (a). |t Guided Torpedo -- |g (b). |t Complete Torpedo-Simulation Program -- |t Stop And Look -- |g 1-17. |t Simulation in the Real World: A Word of Caution -- |t References -- |g ch. 2 |t Models With Difference Equations, Limiters, And Switches -- |t Sampled-Data Systems And Difference Equations -- |g 2-1. |t Sampled-Data Difference-Equation Systems -- |g (a). |t Introduction -- |g (b). |t Difference Equations -- |g (c). |t Minefield of Possible Errors -- |g 2-2. |t Solving Systems of First-Order Difference Equations -- |g (a). |t General Difference-Equation Model -- |g (b). |t Simple Recurrence Relations -- |g 2-3. |t Models Combining Differential Equations and Sampled-Data Operations -- |g 2-4. |t Simple Example -- |g 2-5. |t Initializing and Resetting Sampled-Data Variables -- |t Two Mixed Continuous/Sampled-Data Systems -- |g 2-6. |t Guided Torpedo with Digital Control -- |g 2-7. |t Simulation of a Plant with a Digital PID Controller -- |t Dynamic-System Models With Limiters And Switches -- |g 2-8. |t Limiters, Switches, and Comparators -- |g (a). |t Limiter Functions -- |g (b). |t Switching Functions and Comparators -- |g 2-9. |t Integration of Switch and Limiter Outputs, Event Prediction, and Display Problems -- |g 2-10. |t Using Sampled-Data Assignments -- |g 2-11. |t Using the step Operator and Heuristic Integration-Step Control -- |g 2-12. |t Example: Simulation of a Bang-Bang Servomechanism -- |g 2-13. |t Limiters, Absolute Values, and Maximum/Minimum Selection -- |g 2-14. |t Output-Limited Integration -- |g 2-15. |t Modeling Signal Quantization -- |t Efficient Device Models Using Recursive Assignments -- |g 2-16. |t Recursive Switching and Limiter Operations -- |g 2-17. |t Track/Hold Simulation -- |g 2-18. |t Maximum-Value and Minimum-Value Holding -- |g 2-19. |t Simple Backlash and Hysteresis Models -- |g 2-20. |t Comparator with Hysteresis (Schmitt Trigger) -- |g 2-21. |t Signal Generators and Signal Modulation -- |t References -- |g ch. 3 |t Fast Vector-Matrix Operations And Submodels -- |t Arrays, Vectors, And Matrices -- |g 3-1. |t Arrays and Subscripted Variables -- |g (a). |t Improved Modeling -- |g (b). |t Array Declarations, Vectors, and Matrices -- |g (c). |t State-Variable Declarations -- |g 3-2. |t Vector and Matrices in Experiment Protocols -- |g 3-3. |t Time-History Arrays -- |t Vectors And Model Replication -- |g 3-4. |t Vector Operations in DYNAMIC Program Segments: The Vectorizing Compiler -- |g (a). |t Vector Assignments and Vector Expressions -- |g (b). |t Vector Differential Equations -- |g (c). |t Vector Sampled-Data Assignments and Difference Equations -- |g 3-5. |t Matrix-Vector Products in Vector Expressions -- |g (a). |t Definition -- |g (b). |t Simple Example: Resonating Oscillators -- |g 3-6. |t Index-Shift Operation -- |g (a). |t Definition -- |g (b). |t Preview of Significant Applications -- |g 3-7. |t Sorting Vector and Subscripted-Variable Assignments -- |g 3-8. |t Replication of Dynamic-System Models -- |t More Vector Operations -- |g 3-9. |t Sums, DOT Products, and Vector Norms -- |g (a). |t Sums and DOT Products -- |g (b). |t Euclidean, Taxicab, and Hamming Norms -- |g 3-10. |t Maximum/Minimum Selection and Masking -- |g (a). |t Maximum/Minimum Selection -- |g (b). |t Masking Vector Expressions -- |t Vector Equivalence Declarations Simplify Models -- |g 3-11. |t Subvectors -- |g 3-12. |t Matrix-Vector Equivalence -- |t Matrix Operations In Dynamic-System Models -- |g 3-13. |t Simple Matrix Assignments -- |g 3-14. |t Two-Dimensional Model Replication -- |g (a). |t Matrix Expressions and DOT Products -- |g (b). |t Matrix Differential Equations -- |g (c). |t Matrix Difference Equations -- |t Vectors In Physics And Control-System Problems -- |g 3-15. |t Vectors in Physics Problems -- |g 3-16. |t Vector Model of a Nuclear Reactor -- |g 3-17. |t Linear Transformations and Rotation Matrices -- |g 3-18. |t State-Equation Models of Linear Control Systems -- |t User-Defined Functions And Submodels -- |g 3-19. |t Introduction -- |g 3-20. |t User-Defined Functions -- |g 3-21. |t Submodel Declaration and Invocation -- |g 3-22. |t Dealing with Sampled-Data Assignments, Limiters, and Switches -- |t References -- |g ch. 4 |t Efficient Parameter-Influence Studies And Statistics Computation -- |t Model Replication Simplifies Parameter-Influence Studies -- |g 4-1. |t Exploring the Effects of Parameter Changes -- |g 4-2. |t Repeated Simulation Runs Versus Model Replication -- |g (a). |t Simple Repeated-Run Study -- |g (b). |t Model Replication (Vectorization) -- |g 4-3. |t Programming Parameter-Influence Studies -- |g (a). |t Measures of System Performance -- |g (b). |t Program Design -- |g (c). |t Two-Dimensional Model Replication -- |g (d). |t Cross-Plotting Results -- |g (e). |t Maximum/Minimum Selection -- |g (f). |t Iterative Parameter Optimization -- |t Statistics -- |g 4-4. |t Random Data and Statistics -- |g 4-5. |t Sample Averages and Statistical Relative Frequencies -- |t Computing Statistics By Vector Averaging -- |g 4-6. |t Fast Computation of Sample Averages -- |g 4-7. |t Fast Probability Estimation -- |g 4-8. |t Fast Probability-Density Estimation -- |g (a). |t Simple Probability-Density Estimate -- |g (b). |t Triangle and Parzen Windows -- |g (c). |t Computation and Display of Parzen-Window Estimates -- |g 4-9. |t Sample-Range Estimation -- |t Replicated Averages Generate Sampling Distributions -- |g 4-10. |t Computing Statistics by Time Averaging -- |g 4-11. |t Sample Replication and Sampling-Distribution Statistics -- |g (a). |t Introduction -- |g (b). |t Demonstrations of Empirical Laws of Large Numbers -- |g (c). |t Counterexample: Fat-Tailed Distribution -- |t Random-Process Simulation -- |g 4-12. |t Random Processes and Monte Carlo Simulation -- |g 4-13. |t Modeling Random Parameters and Random Initial Values -- |g 4-14. |t Sampled-Data Random Processes -- |g 4-15. |t "Continuous" Random Processes -- |g (a). |t Modeling Continuous Noise -- |g (b). |t Continuous Time Averaging -- |g (c). |t Correlation Functions and Spectral Densities -- |g 4-16. |t Problems with Simulated Noise -- |t Simple Monte Carlo Experiments -- |g 4-17. |t Introduction -- |g 4-18. |t Gambling Returns -- |g 4-19. |t Vectorized Monte Carlo Study of a Continuous Random Walk -- |t References -- |g ch. |
| 505 | 0 | 0 | |t 5 |t Monte Carlo Simulation Of Real Dynamic Systems -- |t Introduction -- |g 5-1. |t Survey -- |t Repeated-Run Monte Carlo Simulation -- |g 5-2. |t End-of-Run Statistics for Repeated Simulation Runs -- |g 5-3. |t Example: Effects of Gun-Elevation Errors on a 1776 Cannnonball Trajectory -- |g 5-4. |t Sequential Monte Carlo Simulation -- |t Vectorized Monte Carlo Simulation -- |g 5-5. |t Vectorized Monte Carlo Simulation of the 1776 Cannon Shot -- |g 5-6. |t Combined Vectorized and Repeated-Run Monte Carlo Simulation -- |g 5-7. |t Interactive Monte Carlo Simulation: Computing Runtime Histories of Statistics with DYNAMIC-Segment DOT Operations -- |g 5-8. |t Example: Torpedo Trajectory Dispersion -- |t Simulation Of Noisy Control Systems -- |g 5-9. |t Monte Carlo Simulation of a Nonlinear Servomechanism: A Noise-Input Test -- |g 5-10. |t Monte Carlo Study of Control-System Errors Caused by Noise -- |t Additional Topics -- |g 5-11. |t Monte Carlo Optimization -- |g 5-12. |t Convenient Heuristic Method for Testing Pseudorandom Noise -- |g 5-13. |t Alternative to Monte Carlo Simulation -- |g (a). |t Introduction -- |g (b). |t Dynamic Systems with Random Perturbations -- |g (c). |t Mean-Square Errors in Linearized Systems -- |t References -- |g ch. 6 |t Vector Models Of Neural Networks -- |t Artificial Neural Networks -- |g 6-1. |t Introduction -- |g 6-2. |t Artificial Neural Networks -- |g 6-3. |t Static Neural Networks: Training, Validation, and Applications -- |g 6-4. |t Dynamic Neural Networks -- |t Simple Vector Assignments Model Neuron Layers -- |g 6-5. |t Neuron-Layer Declarations and Neuron Operations -- |g 6-6. |t Neuron-Layer Concatenation Simplifies Bias Inputs -- |g 6-7. |t Normalizing and Contrast-Enhancing Layers -- |g (a). |t Pattern Normalization -- |g (b). |t Contrast Enhancement: Softmax and Thresholding -- |g 6-8. |t Multilayer Networks -- |g 6-9. |t Exercising a Neural-Network Model -- |g (a). |t Computing Successive Neuron-Layer Outputs -- |g (b). |t Input from Pattern-Row Matrices -- |g (c). |t Input from Text Files and Spreadsheets -- |t SUPERVISED TRAINING FOR REGRESSION -- |g 6-10. |t Mean-Square Regression -- |g (a). |t Problem Statement -- |g (b). |t Linear Mean-Square Regression and the Delta Rule -- |g (c). |t Nonlinear Neuron Layers and Activation-Function Derivatives -- |g (d). |t Error-Measure Display -- |g 6-11. |t Backpropagation Networks -- |g (a). |t Generalized Delta Rule -- |g (b). |t Momentum Learning -- |g (c). |t Simple Example -- |g (d). |t Classical XOR Problem and Other Examples -- |t More Neural-Network Models -- |g 6-12. |t Functional-Link Networks -- |g 6-13. |t Radial-Basis-Function Networks -- |g (a). |t Basis-Function Expansion and Linear Optimization -- |g (b). |t Radial Basis Functions -- |g 6-14. |t Neural-Network Submodels. |
| 505 | 0 | 0 | |g Note continued: |t Pattern Classification -- |g 6-15. |t Introduction -- |g 6-16. |t Classifier Input from Files -- |g 6-17. |t Classifier Networks -- |g (a). |t Simple Linear Classifiers -- |g (b). |t Softmax Classifiers -- |g (c). |t Backpropagation Classifiers -- |g (d). |t Functional-Link Classifiers -- |g (e). |t Other Classsifiers -- |g 6-18. |t Examples -- |g (a). |t Classification Using an Empirical Database: Fisher's Iris Problem -- |g (b). |t Image-Pattern Recognition and Associative Memory -- |t Pattern Simplification -- |g 6-19. |t Pattern Centering -- |g 6-20. |t Feature Reduction -- |g (a). |t Bottleneck Layers and Encoders -- |g (b). |t Principal Components -- |t Network-Training Problems -- |g 6-21. |t Learning-Rate Adjustment -- |g 6-22. |t Overfitting and Generalization -- |g (a). |t Introduction -- |g (b). |t Adding Noise -- |g (c). |t Early Stopping -- |g (d). |t Regularization -- |g 6-23. |t Beyond Simple Gradient Descent -- |t Unsupervised Competitive-Layer Classifiers -- |g 6-24. |t Template-Pattern Matching and the CLEARN Operation -- |g (a). |t Template Patterns and Template Matrix -- |g (b). |t Matching Known Template Patterns -- |g (c). |t Template-Pattern Training -- |g (d). |t Correlation Training -- |g 6-25. |t Learning with Conscience -- |g 6-26. |t Competitive-Learning Experiments -- |g (a). |t Pattern Classification -- |g (b). |t Vector Quantization -- |g 6-27. |t Simplified Adaptive-Resonance Emulation -- |t Supervised Competitive Learning -- |g 6-28. |t LVQ Algorithm for Two-Way Classification -- |g 6-29. |t Counterpropagation Networks -- |t Examples Of Clearn Classifiers -- |g 6-30. |t Recognition of Known Patterns -- |g (a). |t Image Recognition -- |g (b). |t Fast Solution of the Spiral Benchmark Problem -- |g 6-31. |t Learning Unknown Patterns -- |t References -- |g ch. 7 |t Dynamic Neural Networks -- |t Introduction -- |g 7-1. |t Dynamic Versus Static Neural Networks -- |g 7-2. |t Applications of Dynamic Neural Networks -- |g 7-3. |t Simulations Combining Neural Networks and Differential-Equation Models -- |t Neural Networks With Delay-Line Input -- |g 7-4. |t Introduction -- |g 7-5. |t Delay-Line Model -- |g 7-6. |t Delay-Line-Input Networks -- |g (a). |t Linear Combiners -- |g (b). |t One-Layer Nonlinear Network -- |g (c). |t Functional-Link Network -- |g (d). |t Backpropagation Network with Delay-Line Input -- |g 7-7. |t Using Gamma Delay Lines -- |t Static Neural Networks Used As Dynamic Networks -- |g 7-8. |t Introduction -- |g 7-9. |t Simple Backpropagation Networks -- |t Recurrent Neural Networks -- |g 7-10. |t Layer-Feedback Networks -- |g 7-11. |t Simplified Recurrent-Network Models Combine Context and Input Layers -- |g (a). |t Conventional Model of a Jordan Network -- |g (b). |t Simplified Jordan-Network Model -- |g (c). |t Simplified Models for Other Feedback Networks -- |g 7-12. |t Neural Networks with Feedback Delay Lines -- |g (a). |t Delay-Line Feedback -- |g (b). |t Neural Networks with Both Input and Feedback Delay Lines -- |g 7-13. |t Teacher Forcing -- |t Predictor Networks -- |g 7-14. |t Off-Line Predictor Training -- |g (a). |t Off-Line Prediction Using Stored Time Series -- |g (b). |t Off-Line Training System for Online Predictors -- |g (c). |t Example: Simple Linear Predictor -- |g 7-15. |t Online Trainng for True Online Prediction -- |g 7-16. |t Chaotic Time Series for Prediction Experiments -- |g 7-17. |t Gallery of Predictor Networks -- |t Other Applications Of Dynamic Networks -- |g 7-18. |t Temporal-Pattern Recognition: Regression and Classification -- |g 7-19. |t Model Matching -- |g (a). |t Introduction -- |g (b). |t Example: Program for Matching Narendra's Plant Model -- |t Miscellaneous Topics -- |g 7-20. |t Biological-Network Software -- |t References -- |g ch. 8 |t More Appications Of Vector Models -- |t Vectorized Simulation With Logarithmic Plots -- |g 8-1. |t EUROSIM No. 1 Benchmark Problem -- |g 8-2. |t Vectorized Simulation with Logarithmic Plots -- |t Modeling Fuzzy-Logic Function Generators -- |g 8-3. |t Rule Tables Specify Heuristic Functions -- |g 8-4. |t Fuzzy-Set Logic -- |g (a). |t Fuzzy Sets and Membership Functions -- |g (b). |t Fuzzy Intersections and Unions -- |g (c). |t Joint Membership Functions -- |g (d). |t Normalized Fuzzy-Set Partitions -- |g 8-5. |t Fuzzy-Set Rule Tables and Function Generators -- |g 8-6. |t Simplified Function Generation with Fuzzy Basis Functions -- |g 8-7. |t Vector Models of Fuzzy-Set Partitions -- |g (a). |t Gaussian Bumps: Effects of Normalization -- |g (b). |t Triangle Functions -- |g (c). |t Smooth Fuzzy-Basis Functions -- |g 8-8. |t Vector Models for Multidimensional Fuzzy-Set Partitions -- |g 8-9. |t Example: Fuzzy-Logic Control of a Servomechanism -- |g (a). |t Problem Statement -- |g (b). |t Experiment Protocol and Rule Table -- |g (c). |t DYNAMIC Program Segment and Results -- |t Partial Differential Equations -- |g 8-10. |t Method of Lines -- |g 8-11. |t Vectorized Method of Lines -- |g (a). |t Introduction -- |g (b). |t Using Differentiation Operators -- |g (c). |t Numerical Problems -- |g 8-12. |t Heat-Conduction Equation in Cylindrical Coordinates -- |g 8-13. |t Generalizations -- |g 8-14. |t Simple Heat-Exchanger Model -- |t Fourier Analysis And Linear-System Dynamics -- |g 8-15. |t Introduction -- |g 8-16. |t Function-Table Lookup and Interpolation -- |g 8-17. |t Fast-Fourier-Transform Operations -- |g 8-18. |t Impulse and Freqency Response of a Linear Servomechanism -- |g 8-19. |t Compact Vector Models of Linear Dynamic Systems -- |g (a). |t Using the Index-Shift Operation with Analog Integration -- |g (b). |t Linear Sampled-Data Systems -- |g (c). |t Example: Digital Comb Filter -- |t Replication Of Agroecological Models On Map Grids -- |g 8-20. |t Geographical Information System -- |g 8-21. |t Modeling the Evolution of Landscape Features -- |g 8-22. |t Matrix Operations on a Map Grid -- |t References -- |t APPENDIX: ADDITIONAL REFERENCE MATERIAL -- |g A-1. |t Example of a Radial-Basis-Function Network -- |g A-2. |t Fuzzy-Basis-Function Network. |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a System analysis |x Simulation methods. | |
| 650 | 0 | |a Open source software. | |
| 650 | 0 | |a Computer software |x Development. | |
| 650 | 6 | |a Analyse de systèmes |x Méthodes de simulation. | |
| 650 | 6 | |a Logiciels libres. | |
| 650 | 7 | |a COMPUTERS |x Computer Simulation. |2 bisacsh | |
| 650 | 7 | |a Computer software |x Development |2 fast | |
| 650 | 7 | |a Open source software |2 fast | |
| 650 | 7 | |a System analysis |x Simulation methods |2 fast | |
| 650 | 7 | |a Civil & Environmental Engineering. |2 hilcc | |
| 650 | 7 | |a Engineering & Applied Sciences. |2 hilcc | |
| 650 | 7 | |a Operations Research. |2 hilcc | |
| 776 | 0 | 8 | |i Print version: |a Korn, Granino A. (Granino Arthur), 1922- |t Advanced dynamic-system simulation. |b Second edition. |d Hoboken, New Jersey : John Wiley & Sons Inc., [2012] |z 9781118397350 |w (DLC) 2012034771 |
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