By Granino A. Korn(auth.)
A exact, hands-on consultant to interactive modeling and simulation of engineering systems
This booklet describes complicated, state-of-the-art suggestions for dynamic procedure simulation utilizing the need modeling/simulation software program package deal. It bargains exact advice on the best way to enforce the software program, offering scientists and engineers with robust instruments for developing simulation situations and experiments for such dynamic structures as aerospace cars, regulate platforms, or organic platforms.
Along with new chapters on neural networks, Advanced Dynamic-System Simulation, moment Edition revamps and updates all of the fabric, clarifying motives and including many new examples. A bundled CD comprises an industrial-strength model of OPEN hope in addition to thousands of application examples that readers can use of their personal experiments. the single publication out there to illustrate version replication and Monte Carlo simulation of real-world engineering platforms, this quantity:
- Presents a newly revised systematic strategy for difference-equation modeling
- Covers runtime vector compilation for quick version replication on a private computer
- Discusses parameter-influence reports, introducing very speedy vectorized facts computation
- Highlights Monte Carlo reviews of the consequences of noise and production tolerances for control-system modeling
- Demonstrates quickly, compact vector types of neural networks for keep an eye on engineering
- Features vectorized courses for fuzzy-set controllers, partial differential equations, and agro-ecological modeling
Advanced Dynamic-System Simulation, moment Edition is a very resource for researchers and layout engineers up to speed and aerospace engineering, ecology, and agricultural making plans. it's also an exceptional advisor for college kids utilizing DESIRE.Content:
Chapter 1 Dynamic?System types and Simulation (pages 1–30):
Chapter 2 versions with distinction Equations, Limiters, and Switches (pages 31–55):
Chapter three quick Vector?Matrix Operations and Submodels (pages 57–75):
Chapter four effective Parameter?Influence stories and statistics Computation (pages 77–107):
Chapter five Monte Carlo Simulation of actual Dynamic structures (pages 109–125):
Chapter 6 Vector versions of Neural Networks (pages 127–175):
Chapter 7 Dynamic Neural Networks (pages 177–205):
Chapter eight extra purposes of Vector versions (pages 207–243):
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Additional resources for Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies, Second Edition
Desirable parameter combinations must, in some sense, produce small servo errors. , step inputs, ramps, sinusoids, or noise). Simulations must be repeated with different input amplitudes, since the ﬁeld saturation makes our model nonlinear. Such computer-aided experiments provide some intuitive feel for the control problem and may quickly indicate instability or design errors. For objective decisionmaking, though, we must deﬁne and compute numerical error measures. These are typically functionals determined by the entire time history of the servo error x(t) − u(t) for a given input u(t).
Missile Guidance 12. A. ), Wiley, New York, 1968. 30 CHAPTER 1 DYNAMIC-SYSTEM MODELS AND SIMULATION 13. M: Missile Guidance and Control, Springer-Verlag, New York, 2003. 14. : Guided Missiles: Modern Precision Weapons, Fastbook Publishing, 2008. 15. : Modern Guided Missiles, CRC/Taylor & Francis, Boca Raton, FL, 2008. CHAPTER 2 MODELS WITH DIFFERENCE EQUATIONS, LIMITERS, AND SWITCHES SAMPLED-DATA SYSTEMS AND DIFFERENCE EQUATIONS 2-1. Sampled-Data Difference-Equation Systems1 (a) Introduction Sampled-data assignments model applications such as digital ﬁlters, digital controllers, and neural networks.
50 CHAPTER 2 MODELS WITH DIFFERENCE EQUATIONS, LIMITERS, AND SWITCHES Note in Fig. 2-8 and throughout the remaining sections of this chapter, that COMINT = TMAX/(NN − 1) is so small that y = y(t + COMINT) and yy = y(t) are indis- tinguishable. One can therefore simplify such device models simply by omitting the extra output variable yy. 2-18. Maximum-Value and Minimum-Value Holding (see also Sec. 2-13)  The difference-equation state variable (2-21) max = x + lim(max − x) tracks and holds the largest past value of x = x(t) (Fig.
Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies, Second Edition by Granino A. Korn(auth.)