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"

"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"

5Monte 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. 6Vector 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.

Machine generated contents note:ch. 1Dynamic-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. 2Models 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. 3Fast 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. 4Efficient 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.

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. 7Dynamic 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. 8More 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.

Includes bibliographical references and index.