| Description |
1 online resource |
| Contents |
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 |
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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 |
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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 |
| Summary |
"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"-- Provided by publisher |
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"This book introduces Dynamic-system Simulation with a main emphasis on OPEN DESIRE and DESIRE software"-- Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
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Print version record and CIP data provided by publisher |
| Subject |
System analysis -- Simulation methods
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Open source software.
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Computer software -- Development.
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COMPUTERS -- Computer Simulation.
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Computer software -- Development
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Open source software
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System analysis -- Simulation methods
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Civil & Environmental Engineering.
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Engineering & Applied Sciences.
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Operations Research.
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| Form |
Electronic book
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| LC no. |
2012047703 |
| ISBN |
9781118527443 |
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1118527445 |
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9781118527450 |
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1118527453 |
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9781118527467 |
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1118527461 |
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9781118527412 |
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1118527410 |
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1299189873 |
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9781299189874 |
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1118397355 |
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9781118397350 |
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