Matlib学习资料

chocobocn

APPLIED NUMERICAL METHODS USING MATLAB
Preface xiii
1 MATLAB Usage and Computational Errors 1
1.1 Basic Operations of MATLAB / 1
1.1.1 Input/Output of Data from MATLAB Command
Window / 2
1.1.2 Input/Output of Data Through Files / 2
1.1.3 Input/Output of Data Using Keyboard / 4
1.1.4 2-D Graphic Input/Output / 5
1.1.5 3-D Graphic Output / 10
1.1.6 Mathematical Functions / 10
1.1.7 Operations on Vectors and Matrices / 15
1.1.8 Random Number Generators / 22
1.1.9 Flow Control / 24
1.2 Computer Errors Versus Human Mistakes / 27
1.2.1 IEEE 64-bit Floating-Point Number Representation / 28
1.2.2 Various Kinds of Computing Errors / 31
1.2.3 Absolute/Relative Computing Errors / 33
1.2.4 Error Propagation / 33
1.2.5 Tips for Avoiding Large Errors / 34
1.3 Toward Good Program / 37
1.3.1 Nested Computing for Computational Efficiency / 37
1.3.2 Vector Operation Versus Loop Iteration / 39
1.3.3 Iterative Routine Versus Nested Routine / 40
1.3.4 To Avoid Runtime Error / 40
1.3.5 Parameter Sharing via Global Variables / 44
1.3.6 Parameter Passing Through Varargin / 45
1.3.7 Adaptive Input Argument List / 46
Problems / 46
vii
viii CONTENTS
2 System of Linear Equations 71
2.1 Solution for a System of Linear Equations / 72
2.1.1 The Nonsingular Case (M = N) / 72
2.1.2 The Underdetermined Case (M <N): Minimum-Norm
Solution / 72
2.1.3 The Overdetermined Case (M >N): Least-Squares Error
Solution / 75
2.1.4 RLSE (Recursive Least-Squares Estimation) / 76
2.2 Solving a System of Linear Equations / 79
2.2.1 Gauss Elimination / 79
2.2.2 Partial Pivoting / 81
2.2.3 Gauss–Jordan Elimination / 89
2.3 Inverse Matrix / 92
2.4 Decomposition (Factorization) / 92
2.4.1 LU Decomposition (Factorization):
Triangularization / 92
2.4.2 Other Decomposition (Factorization): Cholesky, QR,
and SVD / 97
2.5 Iterative Methods to Solve Equations / 98
2.5.1 Jacobi Iteration / 98
2.5.2 Gauss–Seidel Iteration / 100
2.5.3 The Convergence of Jacobi and Gauss–Seidel
Iterations / 103
Problems / 104
3 Interpolation and Curve Fitting 117
3.1 Interpolation by Lagrange Polynomial / 117
3.2 Interpolation by Newton Polynomial / 119
3.3 Approximation by Chebyshev Polynomial / 124
3.4 Pade Approximation by Rational Function / 129
3.5 Interpolation by Cubic Spline / 133
3.6 Hermite Interpolating Polynomial / 139
3.7 Two-dimensional Interpolation / 141
3.8 Curve Fitting / 143
3.8.1 Straight Line Fit: A Polynomial Function of First
Degree / 144
3.8.2 Polynomial Curve Fit: A Polynomial Function of Higher
Degree / 145
3.8.3 Exponential Curve Fit and Other Functions / 149
3.9 Fourier Transform / 150
3.9.1 FFT Versus DFT / 151
3.9.2 Physical Meaning of DFT / 152
3.9.3 Interpolation by Using DFS / 155
Problems / 157
CONTENTS ix
4 Nonlinear Equations 179
4.1 Iterative Method Toward Fixed Point / 179
4.2 Bisection Method / 183
4.3 False Position or Regula Falsi Method / 185
4.4 Newton(–Raphson) Method / 186
4.5 Secant Method / 189
4.6 Newton Method for a System of Nonlinear Equations / 191
4.7 Symbolic Solution for Equations / 193
4.8 A Real-World Problem / 194
Problems / 197
5 Numerical Differentiation/Integration 209
5.1 Difference Approximation for First Derivative / 209
5.2 Approximation Error of First Derivative / 211
5.3 Difference Approximation for Second and Higher
Derivative / 216
5.4 Interpolating Polynomial and Numerical Differential / 220
5.5 Numerical Integration and Quadrature / 222
5.6 Trapezoidal Method and Simpson Method / 226
5.7 Recursive Rule and Romberg Integration / 228
5.8 Adaptive Quadrature / 231
5.9 Gauss Quadrature / 234
5.9.1 Gauss–Legendre Integration / 235
5.9.2 Gauss–Hermite Integration / 238
5.9.3 Gauss–Laguerre Integration / 239
5.9.4 Gauss–Chebyshev Integration / 240
5.10 Double Integral / 241
Problems / 244


6 Ordinary Differential Equations 263
6.1 Euler’s Method / 263
6.2 Heun’s Method: Trapezoidal Method / 266
6.3 Runge–Kutta Method / 267
6.4 Predictor–Corrector Method / 269
6.4.1 Adams–Bashforth–Moulton Method / 269
6.4.2 Hamming Method / 273
6.4.3 Comparison of Methods / 274
6.5 Vector Differential Equations / 277
6.5.1 State Equation / 277
6.5.2 Discretization of LTI State Equation / 281

6.5.3 High-Order Differential Equation to State Equation / 283
6.5.4 Stiff Equation / 284
x CONTENTS
6.6 Boundary Value Problem (BVP) / 287
6.6.1 Shooting Method / 287
6.6.2 Finite Difference Method / 290
Problems / 293
7 Optimization 321
7.1 Unconstrained Optimization [L-2, Chapter 7] / 321
7.1.1 Golden Search Method / 321
7.1.2 Quadratic Approximation Method / 323
7.1.3 Nelder–Mead Method [W-8] / 325
7.1.4 Steepest Descent Method / 328
7.1.5 Newton Method / 330
7.1.6 Conjugate Gradient Method / 332
7.1.7 Simulated Annealing Method [W-7] / 334
7.1.8 Genetic Algorithm [W-7] / 338
7.2 Constrained Optimization [L-2, Chapter 10] / 343
7.2.1 Lagrange Multiplier Method / 343
7.2.2 Penalty Function Method / 346
7.3 MATLAB Built-In Routines for Optimization / 350
7.3.1 Unconstrained Optimization / 350
7.3.2 Constrained Optimization / 352
7.3.3 Linear Programming (LP) / 355
Problems / 357
8 Matrices and Eigenvalues 371
8.1 Eigenvalues and Eigenvectors / 371
8.2 Similarity Transformation and Diagonalization / 373
8.3 Power Method / 378
8.3.1 Scaled Power Method / 378
8.3.2 Inverse Power Method / 380
8.3.3 Shifted Inverse Power Method / 380
8.4 Jacobi Method / 381
8.5 Physical Meaning of Eigenvalues/Eigenvectors / 385
8.6 Eigenvalue Equations / 389
Problems / 390
9 Partial Differential Equations 401
9.1 Elliptic PDE / 402
9.2 Parabolic PDE / 406
9.2.1 The Explicit Forward Euler Method / 406
9.2.2 The Implicit Backward Euler Method / 407
CONTENTS xi
9.2.3 The Crank–Nicholson Method / 409
9.2.4 Two-Dimensional Parabolic PDE / 412
9.3 Hyperbolic PDE / 414
9.3.1 The Explicit Central Difference Method / 415
9.3.2 Two-Dimensional Hyperbolic PDE / 417
9.4 Finite Element Method (FEM) for solving PDE / 420
9.5 GUI of MATLAB for Solving PDEs: PDETOOL / 429
9.5.1 Basic PDEs Solvable by PDETOOL / 430
9.5.2 The Usage of PDETOOL / 431
9.5.3 Examples of Using PDETOOL to Solve PDEs / 435
Problems / 444
Appendix A. Mean Value Theorem 461
Appendix B. Matrix Operations/Properties 463
Appendix C. Differentiation with Respect to a Vector 471
Appendix D. Laplace Transform 473
Appendix E. Fourier Transform 475
Appendix F. Useful Formulas 477
Appendix G. Symbolic Computation 481
Appendix H. Sparse Matrices 489
Appendix I. MATLAB 491
References 497
Subject Index 499
Index for MATLAB Routines 503
Index for Tables 509

[ 本帖最后由 chocobocn 于 2008-11-15 23:11 编辑 ]





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chocobocn

Numerical Methods in Engineering with MATLAB®
NumericalMethods in Engineering withMATLAB® is a text for engineering
students and a reference for practicing engineers, especially those
who wish to explore the power and efficiency of MATLAB. The choice of
numerical methods was based on their relevance to engineering problems.
Every method is discussed thoroughly and illustrated with problems
involving both hand computation and programming. MATLAB
M-files accompany each method and are available on the book web
site. This code is made simple and easy to understand by avoiding complex
book-keeping schemes, while maintaining the essential features of
the method. MATLAB, was chosen as the example language because of
its ubiquitous use in engineering studies and practice. Moreover, it is
widely available to students on school networks and through inexpensive
educational versions.MATLAB a popular tool for teaching scientific
computation.
Jaan Kiusalaas is a Professor Emeritus in theDepartment of Engineering
Science and Mechanics at the Pennsylvania State University. He has
taught numerical methods, including finite element and boundary element
methods for over 30 years. He is also the co-author of four
other Books—Engineering Mechanics: Statics, Engineering Mechanics:
Dynamics,Mechanics ofMaterials, and an alternate version of this work
with Python code.

chocobocn

Contents
Acknowledgements xi
1 Introduction 1
1.1 Who this book is for 1
1.2 Why base this book upon MATLAB? 2
1.3 A brief review of the CIE system of colorimetry 4
2 Linear Algebra for Beginners 13
2.1 Some basic definitions 13
2.2 Solving systems of simultaneous equations 14
2.3 Transposes and inverses 16
2.4 Linear and non-linear transforms 16
3 A Short Introduction to MATLAB 19
3.1 Matrix operations 20
3.2 Computing the transpose and inverse of matrices 22
3.3 M-files 25
3.4 Using functions in MATLAB 25
4 Computing CIE Tristimulus Values 27
4.1 Introduction 27
4.2 Standard colour-matching functions 27
4.3 Interpolation methods 29
4.4 Extrapolation methods 33
4.5 Tables of weights 34
4.6 Correction for spectral bandpass 35
4.7 Chromaticity diagrams 35
4.8 Implementations and examples 37
4.8.1 Spectral bandpass correction 37
4.8.2 Reflectance interpolation 39
4.8.3 Computing tristimulus values 41
4.8.4 Plotting the spectral locus 45
5 Computing Colour Difference 49
5.1 Introduction 49
5.2 CIELAB and CIELUV colour space 50
5.3 CIELAB colour difference 52
5.4 Optimised colour-difference formulae 55
5.4.1 CMC(l:c) 55
5.4.2 CIE94 56
5.4.3 CIEDE2000 57
5.5 Implementations and examples 58
5.5.1 Computing CIELAB and CIELUV coordinates 58
5.5.2 Computing colour difference 68
6 Chromatic-adaptation Transforms and Colour Appearance 81
6.1 Introduction 81
6.2 CATs 82
6.2.1 CIECAT94 86
6.2.2 CMCCAT97 89
6.2.3 CMCCAT2000 90
6.3 CAMs 92
6.3.1 CIECAM97s 93
6.3.2 CMCCAM2000 96
6.4 Implementations and examples 96
6.4.1 CATs 96
6.4.2 Computing colour appearance 104
7 Characterization of Computer Displays 111
7.1 Introduction 111
7.2 Gamma 112
7.3 The GOG model 112
7.4 Device-independent transformation 114
7.5 Typical characterization procedure 115
7.6 Implementations and examples 116
8 Characterization of Cameras 127
8.1 Introduction 127
8.2 Correction for non-linearity 128
8.3 Device-independent representation 129
8.4 Implementations and examples 130
9 Characterization of Printers 141
9.1 Introduction 141
9.2 Physical models 142
9.3 Neural networks 143
9.4 Characterization of half-tone printers 145
9.4.1 Correction for non-linearity 145
9.4.2 Device-independent representation 146
9.4.3 Kubelka–Munk model 147
viii CONTENTS
9.5 Implementations and examples 150
9.5.1 Half-tone printer 150
9.5.2 Continuous-tone printer 155
10 Multispectral Imaging 163
10.1 Introduction 163
10.2 Computational colour constancy and linear models 164
10.3 Surface and illuminant estimation algorithms 170
10.4 Techniques for multispectral imaging 171
10.4.1 The Hardeberg method 171
10.4.2 The Imai and Berns method 172
10.4.3 Methods based on maximum smoothness 172
10.5 Implementations and examples 172
10.5.1 Deriving a set of basis functions 172
10.5.2 Representation of reflectance spectra in a linear model 176
10.5.3 Estimation of reflectance spectra from tristimulus values 179
10.5.4 Estimation of reflectance spectra from camera responses 183
10.5.5 Fourier operations on reflectance spectra 185
11 Colour Toolbox 189
11.1 cband.m (Box 1) 189
11.2 pinterp.m (Box 2) 189
11.3 r2xyz.m (Box 3) 190
11.4 plocus.m (Box 4) 190
11.5 xyz2lab.m (Box 5) 190
11.6 lab2xyz.m (Box 6) 190
11.7 xyz2luv.m (Box 7) 191
11.8 car2pol.m (Box 8) 191
11.9 pol2car (Box 9) 191
11.10 cielabde.m (Box 10) 191
11.11 dhpolarity (Box 11) 192
11.12 cmcde.m (Box 12) 192
11.13 cie94de.m (Box 13) 192
11.14 cie00de.m (Box 14) 193
11.15 cmccat97.m (Box 15) 193
11.16 cmccat00.m (Box 16) 193
11.17 ciecam97s.m (Box 17) 194
11.18 gogtest.m (Box 18) 194
11.19 compgog.m (Box 19) 195
11.20 rgb2xyz.m (Box 20) 195
11.21 xyz2rgb.m (Box 21) 195
11.22 compigog (Box 22) 195
11.23 getlincam.m (Box 23) 196
CONTENTS ix
11.24 lincam (Box 24) 196
11.25 gettrc (Box 25) 196
11.26 r2xyz (Box 26) 197
References 199
Index 205

chocobocn

Biosignal and biomedical image processing matlab-based applications (signal processing and communications, 22)

fingjjjjjj

xia look

neverdown

谢谢楼主

tianxian

好东西 大家要顶啊！！！

faullt222

是不是纯英文的啊？没有中文的么

gentlefu

怎么下不了啊

gentlefu

原来是发言太少了……没办法了，请斑竹见谅啊