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Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies [With CDROM]

Medium: Buch
ISBN: 978-1-118-39735-0
Verlag: WILEY
Erscheinungstermin: 03.05.2013
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A unique, hands-on guide to interactive modeling and simulation of engineering systems

This book describes advanced, cutting-edge techniques for dynamic system simulation using the DESIRE modeling/simulation software package. It offers detailed guidance on how to implement the software, providing scientists and engineers with powerful tools for creating simulation scenarios and experiments for such dynamic systems as aerospace vehicles, control systems, or biological systems.

Along with two new chapters on neural networks, Advanced Dynamic-System Simulation, Second Edition revamps and updates all the material, clarifying explanations and adding many new examples. A bundled CD contains an industrial-strength version of OPEN DESIRE as well as hundreds of program examples that readers can use in their own experiments. The only book on the market to demonstrate model replication and Monte Carlo simulation of real-world engineering systems, this volume:

* Presents a newly revised systematic procedure for difference-equation modeling
* Covers runtime vector compilation for fast model replication on a personal computer
* Discusses parameter-influence studies, introducing very fast vectorized statistics computation
* Highlights Monte Carlo studies of the effects of noise and manufacturing tolerances for control-system modeling
* Demonstrates fast, compact vector models of neural networks for control engineering
* Features vectorized programs for fuzzy-set controllers, partial differential equations, and agro-ecological modeling

Advanced Dynamic-System Simulation, Second Edition is a truly useful resource for researchers and design engineers in control and aerospace engineering, ecology, and agricultural planning. It is also an excellent guide for students using DESIRE.

Produkteigenschaften

  • Artikelnummer: 9781118397350
  • Medium: Buch
  • ISBN: 978-1-118-39735-0
  • Verlag: WILEY
  • Erscheinungstermin: 03.05.2013
  • Sprache(n): Englisch
  • Auflage: 2. Auflage 2013
  • Produktform: Gebunden, CDROM, 004
  • Gewicht: 590 g
  • Seiten: 280
  • Format (B x H x T): 163 x 239 x 23 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Autoren

PREFACE xiiiCHAPTER 1 DYNAMIC-SYSTEM MODELS AND SIMULATION 1SIMULATION IS EXPERIMENTATION WITH MODELS 1(a) Difference-Equation Models 2(b) Differential-Equation Models 2(c) Discussion 3ANATOMY OF A SIMULATION RUN 8(a) Euler Integration 10(b) Improved Integration Rules 10SIMPLE APPLICATION PROGRAMS 12(a) Linear Oscillator 12(b) Nonlinear Oscillator: Duffing's Differential Equation 14INRODUCTION TO CONTROL-SYSTEM SIMULATION 21(a) Guided Torpedo 24(b) Complete Torpedo-Simulation Program 26STOP AND LOOK 28References 29CHAPTER 2 MODELS WITH DIFFERENCE EQUATIONS, LIMITERS, AND SWITCHES 31SAMPLED-DATA SYSTEMS AND DIFFERENCE EQUATIONS 31(a) Introduction 31(b) Difference Equations 31(c) A Minefield of Possible Errors 32(a) General Difference-Equation Model 32(b) Simple Recurrence Relations 33TWO MIXED CONTINUOUS/SAMPLED-DATA SYSTEMS 37DYNAMIC-SYSTEM MODELS WITH LIMITERS AND SWITCHES 40(a) Limiter Functions 40(b) Switching Functions and Comparators 42EFFICIENT DEVICE MODELS USING RECURSIVE ASSIGNMENTS 48References 55CHAPTER 3 FAST VECTOR-MATRIX OPERATIONS AND SUBMODELS 57ARRAYS, VECTORS, AND MATRICES 57(a) Improved Modeling 57(b) Array Declarations, Vectors, and Matrices 57(c) State-Variable Declarations 58VECTORS AND MODEL REPLICATION 59(a) Vector Assignments and Vector Expressions 59(b) Vector Differential Equations 60(c) Vector Sampled-Data Assignments and Difference Equations 60(a) Definition 61(b) Simple Example: Resonating Oscillators 61(a) Definition 63(b) Preview of Significant Applications 63MORE VECTOR OPERATIONS 65(a) Sums and DOT Products 65(b) Euclidean, Taxicab, and Hamming Norms 65(a) Maximum/Minimum Selection 66(b) Masking Vector Expressions 66VECTOR EQUIVALENCE DECLARATIONS SIMPLIFY MODELS 67MATRIX OPERATIONS IN DYNAMIC-SYSTEM MODELS 67(a) Matrix Expressions and DOT Products 68(b) Matrix Differential Equations 68(c) Matrix Difference Equations 69VECTORS IN PHYSICS AND CONTROL-SYSTEM PROBLEMS 69USER-DEFINED FUNCTIONS AND SUBMODELS 72References 75CHAPTER 4 EFFICIENT PARAMETER-INFLUENCE STUDIES AND STATISTICS COMPUTATION 77MODEL REPLICATION SIMPLIFIES PARAMETER-INFLUENCE STUDIES 77(a) Simple Repeated-Run Study 78(b) Model Replication (Vectorization) 78(a) Measures of System Performance 80(b) Program Design 81(c) Two-Dimensional Model Replication 81(d) Cross-Plotting Results 82(e) Maximum/Minimum Selection 83(f) Iterative Parameter Optimization 83STATISTICS 84COMPUTING STATISTICS BY VECTOR AVERAGING 85(a) Simple Probability-Density Estimate 86(b) Triangle and Parzen Windows 87(c) Computation and Display of Parzen-Window Estimates 88REPLICATED AVERAGES GENERATE SAMPLING DISTRIBUTIONS 91(a) Introduction 91(b) Demonstrations of Empirical Laws of Large Numbers 93(c) Counterexample: Fat-Tailed Distribution 95RANDOM-PROCESS SIMULATION 95(a) Modeling Continuous Noise 98(b) Continuous Time Averaging 99(c) Correlation Functions and Spectral Densities 100SIMPLE MONTE CARLO EXPERIMENTS 100References 106CHAPTER 5 MONTE CARLO SIMULATION OF REAL DYNAMIC SYSTEMS 109INTRODUCTION 109REPEATED-RUN MONTE CARLO SIMULATION 109VECTORIZED MONTE CARLO SIMULATION 113Cannon Shot 113SIMULATION OF NOISY CONTROL SYSTEMS 119ADDITIONAL TOPICS 123(a) Introduction 123(b) Dynamic Systems with Random Perturbations 123(c) Mean-Square Errors in Linearized Systems 124References 125CHAPTER 6 VECTOR MODELS OF NEURAL NETWORKS 127ARTIFICIAL NEURAL NETWORKS 127SIMPLE VECTOR ASSIGNMENTS MODEL NEURON LAYERS 130(a) Pattern Normalization 131(b) Contrast Enhancement: Softmax and Thresholding 131(a) Computing Successive Neuron-Layer Outputs 132(b) Input from Pattern-Row Matrices 133(c) Input from Text Files and Spreadsheets 133SUPERVISED TRAINING FOR REGRESSION 134(a) Problem Statement 134(b) Linear Mean-Square Regression and the Delta Rule 135(c) Nonlinear Neuron Layers and Activation-Function Derivatives 136(d) Error-Measure Display 136(a) The Generalized Delta Rule 137(b) Momentum Learning 139(c) Simple Example 139(d) The Classical XOR Problem and Other Examples 140MORE NEURAL-NETWORK MODELS 140(a) Basis-Function Expansion and Linear Optimization 142(b) Radial Basis Functions 143PATTERN CLASSIFICATION 146(a) Simple Linear Classifiers 147(b) Softmax Classifiers 148(c) Backpropagation Classifiers 148(d) Functional-Link Classifiers 149(e) Other Classsifiers 149(a) Classification Using an Empirical Database: Fisher's Iris Problem 149(b) Image-Pattern Recognition and Associative Memory 151PATTERN SIMPLIFICATION 155(a) Bottleneck Layers and Encoders 156(b) Principal Components 156NETWORK-TRAINING PROBLEMS 157(a) Introduction 157(b) Adding Noise 158(c) Early Stopping 158(d) Regularization 159UNSUPERVISED COMPETITIVE-LAYER CLASSIFIERS 159(a) Template Patterns and Template Matrix 159(b) Matching Known Template Patterns 160(c) Template-Pattern Training 160(d) Correlation Training 162(a) Pattern Classification 164(b) Vector Quantization 164SUPERVISED COMPETITIVE LEARNING 167EXAMPLES OF CLEARN CLASSIFIERS 168(a) Image Recognition 168(b) Fast Solution of the Spiral Benchmark Problem 169References 174CHAPTER 7 DYNAMIC NEURAL NETWORKS 177INTRODUCTION 177NEURAL NETWORKS WITH DELAY-LINE INPUT 178(a) Linear Combiners 180(b) One-Layer Nonlinear Network 181(c) Functional-Link Network 181(d) Backpropagation Network with Delay-Line Input 182STATIC NEURAL NETWORKS USED AS DYNAMIC NETWORKS 183RECURRENT NEURAL NETWORKS 185(a) Conventional Model of a Jordan Network 185(b) Simplified Jordan-Network Model 186(c) Simplified Models for Other Feedback Networks 187(a) Delay-Line Feedback 187(b) Neural Networks with Both Input and Feedback Delay Lines 188PREDICTOR NETWORKS 189(a) Off-Line Prediction Using Stored Time Series 189(b) Off-Line Training System for Online Predictors 189(c) Example: Simple Linear Predictor 190OTHER APPLICATIONS OF DYNAMIC NETWORKS 199(a) Introduction 201(b) Example: Program for Matching Narendra's Plant Model 201MISCELLANEOUS TOPICS 204References 204CHAPTER 8 MORE APPLICATIONS OF VECTOR MODELS 207VECTORIZED SIMULATION WITH LOGARITHMIC PLOTS 207MODELING FUZZY-LOGIC FUNCTION GENERATORS 209(a) Fuzzy Sets and Membership Functions 210(b) Fuzzy Intersections and Unions 210(c) Joint Membership Functions 213(d) Normalized Fuzzy-Set Partitions 213(a) Gaussian Bumps: Effects of Normalization 215(b) Triangle Functions 215(c) Smooth Fuzzy-Basis Functions 216(a) Problem Statement 217(b) Experiment Protocol and Rule Table 217(c) DYNAMIC Program Segment and Results 220PARTIAL DIFFERENTIAL EQUATIONS 221(a) Introduction 221(b) Using Differentiation Operators 221(c) Numerical Problems 224FOURIER ANALYSIS AND LINEAR-SYSTEM DYNAMICS 229(a) Using the Index-Shift Operation with Analog Integration 232(b) Linear Sampled-Data Systems 235(c) Example: Digital Comb Filter 236REPLICATION OF AGROECOLOGICAL MODELS ON MAP GRIDS 237References 242APPENDIX: ADDITIONAL REFERENCE MATERIAL 245References 248USING THE BOOK CD 251INDEX 253