Siauw / Bayen An Introduction to MATLAB® Programming and Numerical Methods for Engineers

1. Auflage 2014
ISBN: 978-0-12-799914-2
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 340 Seiten

Assuming no prior background in linear algebra or real analysis, An Introduction to MATLAB© Programming and Numerical Methods for Engineers enables you to develop good computational problem solving techniques through the use of numerical methods and the MATLAB© programming environment. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level allowing you to quickly apply results in practical settings. - Tips, warnings, and 'try this' features within each chapter help the reader develop good programming practices - Chapter summaries, key terms, and functions and operators lists at the end of each chapter allow for quick access to important information - At least three different types of end of chapter exercises - thinking, writing, and coding - let you assess your understanding and practice what you've learned

Alexandre Bayen is the Liao-Cho Professor of Engineering at UC Berkeley. He is a Professor of Electrical Engineering and Computer Science, and Civil and Environmental Engineering. He is currently the Director of the Institute of Transportation Studies (ITS). He is also a Faculty Scientist in Mechanical Engineering, at the Lawrence Berkeley National Laboratory (LBNL). He received the Engineering Degree in applied mathematics from the Ecole Polytechnique, France, in 1998, the M.S. and Ph.D. in aeronautics and astronautics from Stanford University in 1998 and 1999 respectively. He was a Visiting Researcher at NASA Ames Research Center from 2000 to 2003. Between January 2004 and December 2004, he worked as the Research Director of the Autonomous Navigation Laboratory at the Laboratoire de Recherches Balistiques et Aerodynamiques, (Ministere de la Defense, Vernon, France), where he holds the rank of Major. He has been on the faculty at UC Berkeley since 2005. Bayen has authored two books and over 200 articles in peer reviewed journals and conferences. He is the recipient of the Ballhaus Award from Stanford University, 2004, of the CAREER award from the National Science Foundation, 2009 and he is a NASA Top 10 Innovators on Water Sustainability, 2010. His projects Mobile Century and Mobile Millennium received the 2008 Best of ITS Award for 'Best Innovative Practice', at the ITS World Congress and a TRANNY Award from the California Transportation Foundation, 2009. Mobile Millennium has been featured more than 200 times in the media, including TV channels and radio stations (CBS, NBC, ABC, CNET, NPR, KGO, the BBC), and in the popular press (Wall Street Journal, Washington Post, LA Times). Bayen is the recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) award from the White House, 2010. He is also the recipient of the Okawa Research Grant Award, the Ruberti Prize from the IEEE, and the Huber Prize from the ASCE.

Autoren/Hrsg.

Siauw, Timmy

Bayen, Alexandre

Leseproben

List of Figures
Fig. 1.1 The MATLAB environment. 4 Fig. 1.2 Truth tables for the logical AND and OR. 10 Fig. 1.3 XOR Truth table. 14 Fig. 3.1 The MATLAB editor. 45 Fig. 3.2 The MATLAB path editor. 54 Fig. 5.1 Note: Histogram may look slightly different based on your computer. 93 Fig. 6.1 Recursion tree for myRecFactorial(3). 97 Fig. 6.2 Recursion Tree for myRecFib(5). 98 Fig. 6.3 Illustration of the Towers of Hanoi. 102 Fig. 6.4 Breakdown of one iteration of the recursive solution of the Towers of Hanoi problem. 102 Fig. 7.1 Profiler overall results. 118 Fig. 7.2 Profiler results for ProfilerTest.m. 118 Fig. 7.3 Improved profiler results for ProfilerTest.m. 120 Fig. 8.1 Various representations of the number 13. 123 Fig. 9.1 Breakpoint inserted at line 8. 141 Fig. 9.2 MATLAB stopped at breakpoint at line 8. 142 Fig. 10.1 File test.txt opened in wordpad for PC. 147 Fig. 10.2 File from previous example, test.xls, opened in Microsoft Excel. 149 Fig. 11.1 Example of plot (x,y) where x and y are vectors. 152 Fig. 11.2 Example of plot of the function f (x) = x2 on the interval [-5, 5]. 152 Fig. 11.3 Example of plot of the parabola f (x) = x2 with a green dashed line style. 154 Fig. 11.4 Other examples of plotting styles, illustrated for f (x) = x2 and f (x) = x3, respectively. 154 Fig. 11.5 Example of use of title, xlabel, and ylabel to annotate Figure 11.4. 155 Fig. 11.6 Example of using sprintf to create a data-specific title for a figure. 156 Fig. 11.7 Example of using legend to create a legend within a figure. 157 Fig. 11.8 Example of customization of a plot by using the axis command to define the size of the display window and using grid on to display a grid. 158 Fig. 11.9 Example of plots respectively obtained with the commands plot, scatter, bar, loglog, and semilogx organized using the subplot command. 160 Fig. 11.10 Example of a three-dimensional plot obtained for the helix (sin(t), cos(t), t) using plot3. 161 Fig. 11.11 Illustration of surface and contour plots. 163 Fig. 11.12 Snapshot from the animation obtained by execution of the code above. 165 Fig. 11.13 Test case for problem.m7 (plotting a polygon with 5 faces). 168 Fig. 11.14 Test case for the function myFunPlotter (f,x) on the function +exp(sin(x)). 169 Fig. 11.15 Test case for the function myPolyPlotter (n,x) used for five polynomials pk (x) = x1 for k = 1, … , 5. 170 Fig. 11.16 Test case for the function mySierpinski (n). 170 Fig. 11.17 Test case for the function myFern (n) with 100 iterations. 171 Fig. 11.18 Test case for myParametricPlotter. 172 Fig. 11.19 Test cases for mySurfacePlotter. 173 Fig. 12.1 Illustration of the process for finding the solution to a linear system. 189 Fig. 13.1 Results from force-displacement experiment for spring (fictional). 202 Fig. 13.2 Illustration of the L2 projection of Y on the range of A. 204 Fig. 13.3 Estimation data and regression curve ˆ(x)=a1x+a2. 207 Fig. 14.1 Illustration of the interpolation problem: estimate the value of a function in between data points. 212 Fig. 14.2 Linear interpolation of the points x = (0, 1, 2) and y = (1, 3, 2). 212 Fig. 14.3 Illustration of cubic spline interpolation. 213 Fig. 14.4 Resulting plot of previous code. Cubic spline interpolation of the points x = (0, 1, 2) and y = (1, 3, 2). 214 Fig. 14.5 Lagrange basis polynomials for test data. By design, Pi (xj) = 1 when i = j, and Pi (xj) = 0 when i ? j. 217 Fig. 14.6 The Lagrange polynomials going through each of the data points. 218 Fig. 15.1 Successive orders of approximation of the sin function by its Taylor expansion. 227 Fig. 15.2 Successive levels of zoom of a smooth function to illustrate the linear nature of functions locally. 229 Fig. 16.1 Illustration of intermediate value theorem. If sign(f(a)) ? sign(f(b)), then ?c ? (a, b) such that f(c) = 0. 235 Fig. 16.2 Illustration of the bisection method. 236 Fig. 16.3 Illustration of Newton step for a smooth function, g(x). 238 Fig. 17.1 Numerical grid used to approximate functions. 246 Fig. 17.2 Illustration of the forward difference, the backward difference, and the central difference methods. 248 Fig. 17.3 Comparison of the numerical evaluation of the explicit formula for the derivative of cos and of the derivative of cos obtained by the forward difference formula. 250 Fig. 17.4 Maximum error between the numerical evaluation of the explicit formula for the derivative of cos and the derivative of cos obtained by forward finite differencing. 251 Fig. 17.5 Cosine wave contaminated with a small amount of noise. The noise is hardly visible, but it will be shown that it has drastic consequences for the derivative. 253 Fig. 17.6 Noise in numerical derivatives. 253 Fig. 18.1 Illustration of the integral. 260 Fig. 18.2 Illustration of the trapezoid integral procedure. 263 Fig. 18.3 Illustration of the Simpson integral formula. 266 Fig. 18.4 Illustration of the accounting procedure to approximate the function f by the Simpson rule for the entire interval [a, b]. 267 Fig. 18.5 Illustration of a primitive of the function sin computed numerically in the interval [0, p] using the cumtrapz function. 271 Fig. 19.1 Pendulum system. 278 Fig. 19.2 Comparison of the approximate integration of the function (t)dt=e-t between 0 and 1 (dashed) and the exact integration (solid) using Euler’s Explicit Formula. 283 Fig. 19.3 Comparison of the approximate integration of...