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El. knyga: Measurements, Modelling and Simulation of Dynamic Systems

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  • Formatas: PDF+DRM
  • Išleidimo metai: 30-Dec-2009
  • Leidėjas: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Kalba: eng
  • ISBN-13: 9783642045882
  • Formatas: PDF+DRM
  • Išleidimo metai: 30-Dec-2009
  • Leidėjas: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Kalba: eng
  • ISBN-13: 9783642045882

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A close look at analog-to-digital systems offers insight on dynamic measurement methods in this concise introduction. The construction and properties of measurement sensors are analyzed, as these represent the primary components for all measurement systems.

This book discusses an analog-to-digital system intended to dynamic measurement, particularly for non-electrical quantities. The construction and properties of measurement sensors are analyzed in detail, as these represent the primary components for all measurement systems. Procedures for signal noise reduction are presented based on the time window function and a digital Kalman filter. Also covered in this book are the methods of modeling, model development and identification procedures on the basis of measurement data.The theory of maximum errors is applied in order to determine mapping errors of models in case of non-standard input signals. This is based on signals maximizing the chosen error functional. The existence and attainability of such signals is proved and the algorithms for their determination are presented.Detailed calculation methods, based on dedicated numerical procedures are demonstrated, which allow the integral-square error as well as the absolute error to be determined.The problems presented in the book are relevant to a wide range of applications where there is a requirement to determine the accuracy of indeterminate dynamic signals such as occurs in the fields of engineering, medicine, biology, physics etc.This book will interest researchers, scientists, engineers and graduate students in many disciplines, who make use of measurements, modelling and computer simulation.

Recenzijos

From the reviews:

This textbook gives a short introduction to measurements, mathematical modeling, and computer simulation of dynamic systems. The mathematical modeling of dynamic systems is mainly based on systems of linear ordinary differential equations. This book is divided into 5 chapters. mainly written for students and researchers in engineering disciplines, who are interested in measurements, mathematical modeling, and computer simulation of dynamic systems. (Manfred Tasche, Zentralblatt MATH, Vol. 1185, 2010)

Introduction to Measuring Systems
1(28)
Sensor
3(1)
Transducer
3(1)
Matching Circuit
3(1)
Anti-aliasing Filter
3(3)
Multiplexers/Demultiplexers
6(2)
Sample-and-Hold Circuit
8(2)
Analog-to-Digital Conversion
10(14)
A/D Converter with Parallel Comparison
10(2)
A/D Converter with Successive Approximation
12(5)
Integrating A/D Converters
17(5)
Sigma Delta A/D Converter
22(2)
Input Register
24(1)
Digital-to-Analogue Conversion
25(1)
Reconstruction Filter
26(1)
DSP
27(1)
Control System
28(1)
References
28(1)
Sensors
29(34)
Strain Gauge Sensors
29(9)
Temperature Compensation
31(2)
Lead Wires Effect
33(1)
Force Measurement
34(1)
Torque Measurement
35(1)
Pressure Measurement
35(3)
Capacitive Sensors
38(2)
Inductive Sensors
40(5)
Temperature Sensors
45(6)
Vibration Sensors
51(5)
Accelerometer
51(3)
Vibrometer
54(2)
Piezoelectric Sensors
56(3)
Binary-Coded Sensors
59(4)
References
62(1)
Methods of Noise Reduction
63(20)
Weighted Mean Method
63(2)
Windows
65(2)
Effect of Averaging Process on Signal Distortion
67(5)
Efficiency Analysis of Noise Reduction by Means of Filtering
72(6)
Kalman Filter
78(5)
References
82(1)
Model Development
83(44)
Lagrange Polynomials
84(2)
Tchebychev Polynomials
86(4)
Legendre Polynomials
90(3)
Hermite Polynomials
93(2)
Cubic Splines
95(6)
The Least-Squares Approximation
101(1)
Relations between Coefficients of the Models
102(3)
Standard Nets
105(6)
Levenberg-Marquardt Algorithm
111(4)
Implementing Levenberg-Marquardt Algorithm Using Lab VIEW
113(2)
Black-Box Identification
115(2)
Implementing Black-Box Identification Using MATLAB
117(6)
Monte Carlo Method
123(4)
References
124(3)
Mapping Error
127(24)
General Assumption
127(1)
Signals Maximizing the Integral Square Error
128(12)
Existence and Availability of Signals with Two Constraints
128(2)
Signals with Constraint on Magnitude
130(1)
Algorithm for Determining Signals Maximizing the Integral Square Error
131(3)
Signals with Two Constraints
134(5)
Estimation of the Maximum Value of Integral Square Error
139(1)
Signals Maximizing the Absolute Value of Error
140(8)
Signals with Constraint on Magnitude
140(1)
Shape of Signals with Two Constraints
140(8)
Constraints of Signals
148(3)
References
149(2)
Index 151